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diff --git a/TBXTools.py b/TBXTools.py
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+#    TBXTools
+#    version: 2021/11/23
+#    Copyright: Antoni Oliver (2021) - Universitat Oberta de Catalunya - aoliverg@uoc.edu
+#    This program is free software: you can redistribute it and/or modify
+#    it under the terms of the GNU General Public License as published by
+#    the Free Software Foundation, either version 3 of the License, or
+#    (at your option) any later version.
+
+#    This program is distributed in the hope that it will be useful,
+#    but WITHOUT ANY WARRANTY; without even the implied warranty of
+#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#    GNU General Public License for more details.
+
+#    You should have received a copy of the GNU General Public License
+#    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+import os
+import codecs
+import sqlite3
+import xml.etree.cElementTree as etree
+
+import nltk
+from nltk.util import ngrams
+from nltk.probability import FreqDist
+from nltk.collocations import *
+import re
+import pickle
+import gzip
+import operator
+import sys
+import math
+
+import string
+
+import importlib
+
+import gensim
+from gensim.models import Word2Vec
+from gensim.models import KeyedVectors
+import numpy
+
+try:
+    import map_embeddings
+except:
+    pass
+try:
+    import embeddings
+except:
+    pass
+
+class TBXTools:
+    '''Class for automatic terminology extraction and terminology management.'''
+    def __init__(self):
+        self.maxinserts=10000 #controls the maximum number of inserts in memory
+        self.sl_lang=""
+        self.tl_lang=""
+        self.max_id_corpus=0
+        
+        self.sl_stopwords=[]
+        self.tl_stopwords=[]
+        self.sl_inner_stopwords=[]
+        self.tl_inner_stopwords=[]
+        self.sl_exclsions_regexps=[]
+        self.tl_exclusion_regexps=[]
+        self.sl_morphonorm_rules=[]
+        self.tl_morphonorm_rules=[]
+        self.evaluation_terms={}
+        self.tsr_terms=[]
+        self.exclusion_terms={}
+        self.exclusion_no_terms={}
+        self.ngrams={}
+        self.tagged_ngrams={}
+        self.term_candidates={}
+        self.linguistic_patterns={}
+        
+        self.knownterms=[]
+        self.n_min=1
+        self.n_max=5
+        
+        self.punctuation=string.punctuation
+        self.sl_stopwords.extend(self.punctuation)
+        self.tl_stopwords.extend(self.punctuation)
+        self.sl_inner_stopwords.extend(self.punctuation)
+        self.tl_inner_stopwords.extend(self.punctuation)
+        
+        self.specificSLtokenizer=False
+        self.specificTLtokenizer=False
+        
+        self.SLtokenizer=None
+        self.TLtokenizer=None
+        
+        
+    def create_project(self,project_name,sl_lang,tl_lang="null",overwrite=False):
+        '''Opens a project. If the project already exists, it raises an exception. To avoid the exception use overwrite=True. To open existing projects, use the open_project method.'''
+        if os.path.isfile(project_name) and not overwrite:
+                raise Exception("This file already exists")
+        
+        else:
+            if os.path.isfile(project_name) and overwrite:
+                os.remove(project_name)
+            self.sl_lang=sl_lang
+            self.tl_lang=tl_lang
+            self.conn=sqlite3.connect(project_name)
+            self.cur = self.conn.cursor() 
+            self.cur2 = self.conn.cursor()
+            with self.conn:
+                self.cur = self.conn.cursor()
+                self.cur.execute("CREATE TABLE configuration(id INTEGER PRIMARY KEY, sl_lang TEXT, tl_lang TEXT)")
+                self.cur.execute("CREATE TABLE sl_corpus(id INTEGER PRIMARY KEY, segment TEXT)")
+                self.cur.execute("CREATE TABLE tl_corpus(id INTEGER PRIMARY KEY, segment TEXT)")
+                self.cur.execute("CREATE TABLE sl_tagged_corpus(id INTEGER PRIMARY KEY, tagged_segment TEXT)")
+                self.cur.execute("CREATE TABLE tl_tagged_corpus(id INTEGER PRIMARY KEY, tagged_segment TEXT)")
+                self.cur.execute("CREATE TABLE sl_stopwords (id INTEGER PRIMARY KEY AUTOINCREMENT, sl_stopword TEXT)")
+                self.cur.execute("CREATE TABLE sl_inner_stopwords (id INTEGER PRIMARY KEY AUTOINCREMENT, sl_inner_stopword TEXT)")
+                self.cur.execute("CREATE TABLE tl_stopwords (id INTEGER PRIMARY KEY AUTOINCREMENT, tl_stopword TEXT)")
+                self.cur.execute("CREATE TABLE tl_inner_stopwords (id INTEGER PRIMARY KEY AUTOINCREMENT, tl_inner_stopword TEXT)")
+                self.cur.execute("CREATE TABLE sl_exclusion_regexps (id INTEGER PRIMARY KEY AUTOINCREMENT, sl_exclusion_regexp TEXT)")
+                self.cur.execute("CREATE TABLE tl_exclusion_regexps (id INTEGER PRIMARY KEY AUTOINCREMENT, tl_exclusion_regexp TEXT)")
+                self.cur.execute("CREATE TABLE sl_morphonorm_rules (id INTEGER PRIMARY KEY AUTOINCREMENT, sl_morphonorm_rule TEXT)")
+                self.cur.execute("CREATE TABLE tl_morphonorm_rules (id INTEGER PRIMARY KEY AUTOINCREMENT, tl_morphonorm_rule TEXT)")
+                self.cur.execute("CREATE TABLE sl_patterns (id INTEGER PRIMARY KEY AUTOINCREMENT, sl_pattern TEXT)")
+                self.cur.execute("CREATE TABLE tl_patterns (id INTEGER PRIMARY KEY AUTOINCREMENT, tl_pattern TEXT)")
+                self.cur.execute("CREATE TABLE evaluation_terms (id INTEGER PRIMARY KEY AUTOINCREMENT, sl_term TEXT, tl_term TEXT)")
+                self.cur.execute("CREATE TABLE compoundify_terms_sl (id INTEGER PRIMARY KEY AUTOINCREMENT, term TEXT)")
+                self.cur.execute("CREATE TABLE compoundify_terms_tl (id INTEGER PRIMARY KEY AUTOINCREMENT, term TEXT)")
+                self.cur.execute("CREATE TABLE tsr_terms (id INTEGER PRIMARY KEY AUTOINCREMENT, term TEXT)")
+                self.cur.execute("CREATE TABLE exclusion_terms (id INTEGER PRIMARY KEY AUTOINCREMENT, sl_term TEXT, tl_term TEXT)")
+                self.cur.execute("CREATE TABLE exclusion_noterms (id INTEGER PRIMARY KEY AUTOINCREMENT, sl_term TEXT, tl_term TEXT)")
+                self.cur.execute("CREATE TABLE tokens (id INTEGER PRIMARY KEY AUTOINCREMENT, token TEXT, frequency INTEGER)")
+                self.cur.execute("CREATE TABLE ngrams (id INTEGER PRIMARY KEY AUTOINCREMENT, ngram TEXT, n INTEGER, frequency INTEGER)")
+                self.cur.execute("CREATE TABLE tagged_ngrams (id INTEGER PRIMARY KEY AUTOINCREMENT, ngram TEXT, tagged_ngram TEXT, n INTEGER, frequency INTEGER)")
+                
+                self.cur.execute("CREATE INDEX indextaggedngram on tagged_ngrams (ngram);")
+                
+                self.cur.execute("CREATE TABLE embeddings_sl (id INTEGER PRIMARY KEY AUTOINCREMENT, candidate TEXT, embedding BLOB)")
+                self.cur.execute("CREATE INDEX indexembeddings_sl on embeddings_sl (candidate);")
+                
+                self.cur.execute("CREATE TABLE embeddings_sl_ref (id INTEGER PRIMARY KEY AUTOINCREMENT, candidate TEXT, embedding BLOB)")
+                self.cur.execute("CREATE INDEX indexembeddings_sl_ref on embeddings_sl_ref (candidate);")
+                
+                self.cur.execute("CREATE TABLE embeddings_tl (id INTEGER PRIMARY KEY AUTOINCREMENT, candidate TEXT, embedding BLOB)")
+                self.cur.execute("CREATE INDEX indexembeddings_tl on embeddings_tl (candidate);")
+                
+                self.cur.execute("CREATE TABLE term_candidates (id INTEGER PRIMARY KEY AUTOINCREMENT, candidate TEXT, n INTEGER, frequency INTEGER, measure TEXT, value FLOAT)")
+                self.cur.execute("CREATE TABLE index_pt(id INTEGER PRIMARY KEY AUTOINCREMENT, source TEXT, target TEXT, probability FLOAT)")
+                self.cur.execute("CREATE INDEX index_index_pt on index_pt (source);")
+                self.cur.execute("CREATE TABLE linguistic_patterns (id INTEGER PRIMARY KEY AUTOINCREMENT, linguistic_pattern TEXT)")
+                
+                self.cur.execute("INSERT INTO configuration (id, sl_lang, tl_lang) VALUES (?,?,?)",[0,self.sl_lang,self.tl_lang])
+                self.conn.commit()
+                
+    def open_project(self,project_name):
+        '''Opens an existing project. If the project doesn't exist it raises an exception.'''
+        if not os.path.isfile(project_name):
+                raise Exception("Project not found")
+        else:
+            self.conn=sqlite3.connect(project_name)
+            self.cur = self.conn.cursor() 
+            self.cur2 = self.conn.cursor()
+            self.maxsc=0
+            self.maxtc=0
+            with self.conn:
+                self.cur.execute('SELECT sl_lang from configuration where id=0')
+                self.sl_lang=self.cur.fetchone()[0]
+                self.cur.execute('SELECT tl_lang from configuration where id=0')
+                self.tl_lang=self.cur.fetchone()[0]
+                #Maximum position in corpora
+                self.maxpositions=[]
+                self.cur.execute('SELECT max(id) from sl_corpus')
+                self.qresult=self.cur.fetchone()[0]
+                if not self.qresult==None:
+                    self.maxpositions.append(self.qresult)
+                else:
+                    self.maxpositions.append(0)
+                self.cur.execute('SELECT max(id) from tl_corpus')
+                self.qresult=self.cur.fetchone()[0]
+                if not self.qresult==None:
+                    self.maxpositions.append(self.qresult)
+                else:
+                    self.maxpositions.append(0)
+                self.cur.execute('SELECT max(id) from sl_tagged_corpus')
+                self.qresult=self.cur.fetchone()[0]
+                if not self.qresult==None:
+                    self.maxpositions.append(self.qresult)
+                else:
+                    self.maxpositions.append(0)
+                self.cur.execute('SELECT max(id) from tl_tagged_corpus')
+                self.qresult=self.cur.fetchone()[0]
+                if not self.qresult==None:
+                    self.maxpositions.append(self.qresult)
+                else:
+                    self.maxpositions.append(0)
+                self.max_id_corpus=max(self.maxpositions)
+
+                
+                #loading of stopwords
+                self.cur.execute('SELECT sl_stopword from sl_stopwords')
+                self.data=self.cur.fetchall()
+                for self.d in self.data:
+                    self.sl_stopwords.append(self.d[0])
+
+                self.cur.execute('SELECT tl_stopword from tl_stopwords')
+                self.data=self.cur.fetchall()
+                for self.d in self.data:
+                    self.tl_stopwords.append(self.d[0])
+
+                self.cur.execute('SELECT sl_inner_stopword from sl_inner_stopwords')
+                self.data=self.cur.fetchall()
+                for self.d in self.data:
+                    self.sl_inner_stopwords.append(self.d[0])
+
+                self.cur.execute('SELECT tl_inner_stopword from tl_inner_stopwords')
+                self.data=self.cur.fetchall()
+                for self.d in self.data:
+                    self.tl_inner_stopwords.append(self.d[0])
+                    
+                self.cur.execute('SELECT sl_exclusion_regexp from sl_exclusion_regexps')
+                self.data=self.cur.fetchall()
+                for self.d in self.data:
+                    self.sl_exclusion_regexps.append(self.d[0])
+                    
+                self.cur.execute('SELECT tl_exclusion_regexp from tl_exclusion_regexps')
+                self.data=self.cur.fetchall()
+                for self.d in self.data:
+                    self.tl_exclusion_regexps.append(self.d[0])
+                    
+                self.cur.execute('SELECT sl_morphonorm_rule from sl_morphonorm_rules')
+                self.data=self.cur.fetchall()
+                for self.d in self.data:
+                    self.sl_morphonorm_rules.append(self.d[0])
+                
+                self.cur.execute('SELECT tl_morphonorm_rule from tl_morphonorm_rules')
+                self.data=self.cur.fetchall()
+                for self.d in self.data:
+                    self.tl_morphonorm_rules.append(self.d[0])
+                    
+                self.cur.execute('SELECT sl_term,tl_term from evaluation_terms')
+                self.data=self.cur.fetchall()
+                for self.d in self.data:
+                    self.evaluation_terms[self.d[0]]=self.d[1]
+
+    #METODES DELETES
+    def delete_sl_corpus(self):
+        '''Deletes de source language corpus.'''
+        with self.conn:
+            self.cur.execute('DELETE FROM sl_corpus')
+            self.conn.commit()
+    
+    def delete_tl_corpus(self):
+        '''Deletes de target language corpus.'''
+        with self.conn:
+            self.cur.execute('DELETE FROM tl_corpus')
+            self.conn.commit()
+    
+    def delete_sl_tagged_corpus(self):
+        '''Deletes the source language tagged corpus.'''
+        with self.conn:
+            self.cur.execute('DELETE FROM sl_tagged_corpus')
+            self.conn.commit()
+    
+    def delete_tl_tagged_corpus(self):
+        '''Deletes the target language tagged corpu.'''
+        with self.conn:
+            self.cur.execute('DELETE FROM tl_tagged_corpus')
+            self.conn.commit()
+    
+    def delete_sl_stopwords(self):
+        '''Deletes the stop-words for the source language.'''
+        self.sl_stopwords=[]
+        with self.conn:
+            self.cur.execute('DELETE FROM sl_stopwords')
+            self.conn.commit()
+            
+    def delete_tl_stopwords(self):
+        '''Deletes the stop-words fot the target language.'''
+        self.tl_stopwords=[]
+        with self.conn:
+            self.cur.execute('DELETE FROM tl_stopwords')
+            self.conn.commit()
+            
+    def delete_sl_inner_stopwords(self):
+        '''Deletes the inner stop-words for the source language.'''
+        self.sl_inner_stopwords=[]
+        with self.conn:
+            self.cur.execute('DELETE FROM sl_inner_stopwords')
+            self.conn.commit()
+            
+    def delete_tl_inner_stopwords(self):
+        '''Deletes the innter stop-words for the target language.'''
+        self.tl_inner_stopwords=[]
+        with self.conn:
+            self.cur.execute('DELETE FROM tl_inner_stopwords')
+            self.conn.commit()
+    
+    def delete_sl_exclusion_regexps(self):
+        '''Deletes the exclusion regular expressions for the source language.'''
+        self.sl_exclusion_regexps=[]
+        with self.conn:
+            self.cur.execute('DELETE FROM sl_exclusion_regexps')
+            self.conn.commit()
+    
+    def delete_tl_exclusion_regexps(self):
+        '''Deletes the exclusion regular expressions for the target language.'''
+        self.tl_exclusion_regexps=[]
+        with self.conn:
+            self.cur.execute('DELETE FROM tl_exclusion_regexps')
+            self.conn.commit()
+            
+    def delete_sl_morphonorm_rules(self):
+        '''Deletes the morphological normalisation rules for the source language.'''
+        self.sl_morphonorm_rules=[]
+        with self.conn:
+            self.cur.execute('DELETE FROM sl_morphonorm_rules')
+            self.conn.commit()
+            
+    def delete_tl_morphonorm_rules(self):
+        '''Deletes the morphological normalisation rules for the target language.'''
+        self.tl_morphonorm_rules=[]
+        with self.conn:
+            self.cur.execute('DELETE FROM tl_morphonorm_rules')
+            self.conn.commit()
+
+    def delete_evaluation_terms(self):
+        '''Deletes the evaluation terms.'''
+        self.evaluation_terms={}
+        with self.conn:
+            self.cur.execute('DELETE FROM evaluation_terms')
+            self.conn.commit()
+            
+    def delete_exclusion_terms(self):
+        '''Deletes the exclusion terms.'''
+        self.exclusion_terms={}
+        with self.conn:
+            self.cur.execute('DELETE FROM exclusion_terms')
+            self.conn.commit()   
+            
+    def delete_exclusion_no_terms(self):
+        '''Deletes the exclusion no terms.'''
+        self.exclusion_terms={}
+        with self.conn:
+            self.cur.execute('DELETE FROM exclusion_terms')
+            self.conn.commit() 
+            
+    def delete_linguistic_patterns(self):
+        '''Deletes the linguistic patterns for linguistic terminology extraction.'''
+        self.exclusion_terms={}
+        with self.conn:
+            self.cur.execute('DELETE FROM exclusion_terms')
+            self.conn.commit() 
+    
+    def delete_tokens(self):
+        '''Deletes the tokens,'''
+        self.ngrams={}
+        with self.conn:
+            self.cur.execute('DELETE FROM tokens')
+            self.conn.commit()
+            
+    def delete_ngrams(self):
+        '''Deletes the ngrams.'''
+        self.ngrams={}
+        with self.conn:
+            self.cur.execute('DELETE FROM ngrams')
+            self.conn.commit()
+            
+    def delete_tagged_ngrams(self):
+        '''Deletes the tagged ngrams.'''
+        self.tagged_ngrams={}
+        with self.conn:
+            self.cur.execute('DELETE FROM tagged_ngrams')
+            self.conn.commit()
+    
+    def delete_term_candidates(self):
+        '''Deletes the term candidates.'''
+        self.term_candidates={}
+        with self.conn:
+            self.cur.execute('DELETE FROM term_candidates')
+            self.conn.commit()
+                
+    
+    def load_sl_corpus(self,corpusfile, encoding="utf-8", compoundify=False):
+        '''Loads a monolingual corpus for the source language. It's recommended, but not compulsory, that the corpus is segmented (one segment per line). Use TBXTools external tools to segment the corpus. A plain text corpus (not segmented), can be aslo used.'''
+        if compoundify:
+            compterms=[]
+            self.cur.execute('SELECT term from compoundify_terms_sl')
+            data=self.cur.fetchall()
+            for d in data:
+                compterms.append(d[0])
+            
+        self.cf=codecs.open(corpusfile,"r",encoding=encoding,errors="ignore")
+        self.data=[]
+        self.continserts=0
+        for self.line in self.cf:
+            
+            self.continserts+=1
+            self.max_id_corpus+=1
+            self.record=[]
+            self.line=self.line.rstrip()
+            if compoundify:
+                for compterm in compterms:
+                    if self.line.find(compterm)>=1:
+                        comptermMOD=compterm.replace(" ","▁")
+                        self.line=self.line.replace(compterm,comptermMOD)
+            self.record.append(self.max_id_corpus)
+            self.record.append(self.line)
+            self.data.append(self.record)
+            if self.continserts==self.maxinserts:
+                self.cur.executemany("INSERT INTO sl_corpus (id, segment) VALUES (?,?)",self.data)
+                self.data=[]
+                self.continserts=0
+        with self.conn:
+            self.cur.executemany("INSERT INTO sl_corpus (id, segment) VALUES (?,?)",self.data)    
+        self.conn.commit()
+        
+    def load_tl_corpus(self,corpusfile, encoding="utf-8", compoundify=False):
+        '''Loads a monolingual corpus for the target language. It's recommended, but not compulsory, that the corpus is segmented (one segment per line). Use TBXTools external tools to segment the corpus. A plain text corpus (not segmented), can be aslo used.'''
+        if compoundify:
+            compterms=[]
+            self.cur.execute('SELECT term from compoundify_terms_tl')
+            data=self.cur.fetchall()
+            for d in data:
+                compterms.append(d[0])
+        self.cf=codecs.open(corpusfile,"r",encoding=encoding,errors="ignore")
+        self.data=[]
+        self.continserts=0
+        for self.line in self.cf:
+            
+            self.continserts+=1
+            self.max_id_corpus+=1
+            self.record=[]
+            self.line=self.line.rstrip()
+            if compoundify:
+                for compterm in compterms:
+                    if self.line.find(compterm)>=1:
+                        comptermMOD=compterm.replace(" ","▁")
+                        self.line=self.line.replace(compterm,comptermMOD)
+            self.record.append(self.max_id_corpus)
+            self.record.append(self.line)
+            self.data.append(self.record)
+            if self.continserts==self.maxinserts:
+                self.cur.executemany("INSERT INTO tl_corpus (id, segment) VALUES (?,?)",self.data)
+                self.data=[]
+                self.continserts=0
+        with self.conn:
+            self.cur.executemany("INSERT INTO tl_corpus (id, segment) VALUES (?,?)",self.data)    
+        self.conn.commit()
+    
+    def load_sl_corpus_ref(self,corpusfile, encoding="utf-8", compoundify=False):
+        '''Loads a monolingual corpus for the source language. It's recommended, but not compulsory, that the corpus is segmented (one segment per line). Use TBXTools external tools to segment the corpus. A plain text corpus (not segmented), can be aslo used.'''
+        if compoundify:
+            compterms=[]
+            self.cur.execute('SELECT term from compoundify_terms_sl')
+            data=self.cur.fetchall()
+            for d in data:
+                compterms.append(d[0])
+        self.cf=codecs.open(corpusfile,"r",encoding=encoding,errors="ignore")
+        self.data=[]
+        self.continserts=0
+        for self.line in self.cf:
+            
+            self.continserts+=1
+            self.max_id_corpus+=1
+            self.record=[]
+            self.line=self.line.rstrip()
+            if compoundify:
+                for compterm in compterms:
+                    if self.line.find(compterm)>=1:
+                        comptermMOD=compterm.replace(" ","▁")
+                        self.line=self.line.replace(compterm,comptermMOD)
+            self.record.append(self.max_id_corpus)
+            self.record.append(self.line)
+            self.data.append(self.record)
+            if self.continserts==self.maxinserts:
+                self.cur.executemany("INSERT INTO sl_corpus_ref (id, segment) VALUES (?,?)",self.data)
+                self.data=[]
+                self.continserts=0
+        with self.conn:
+            self.cur.executemany("INSERT INTO sl_corpus_ref (id, segment) VALUES (?,?)",self.data)    
+        self.conn.commit()
+        
+    def load_sl_tl_corpus(self,slcorpusfile, tlcorpusfile, encoding="utf-8"):
+        '''Loads a bilingual corpus in Moses format (that is, in two independent files. It expects one segment per line.'''
+        self.slcf=codecs.open(slcorpusfile,"r",encoding=encoding)
+        self.tlcf=codecs.open(tlcorpusfile,"r",encoding=encoding)
+        self.sl_data=[]
+        self.tl_data=[]
+        self.continserts=0
+        while 1:
+            self.sl_segment=self.slcf.readline()
+            if not self.sl_segment:
+                break
+            self.tl_segment=self.tlcf.readline()
+            self.continserts+=1
+            self.max_id_corpus+=1
+            self.sl_record=[]
+            self.tl_record=[]
+            self.sl_segment=self.sl_segment.rstrip()
+            self.tl_segment=self.tl_segment.rstrip()
+            
+            self.sl_record.append(self.max_id_corpus)
+            self.tl_record.append(self.max_id_corpus)
+            self.sl_record.append(self.sl_segment)
+            self.tl_record.append(self.tl_segment)
+            self.sl_data.append(self.sl_record)
+            self.tl_data.append(self.tl_record)
+            if self.continserts==self.maxinserts:
+                self.cur.executemany("INSERT INTO sl_corpus (id, segment) VALUES (?,?)",self.sl_data)
+                self.cur.executemany("INSERT INTO tl_corpus (id, segment) VALUES (?,?)",self.tl_data)
+                self.sl_data=[]
+                self.tl_data=[]
+                self.continserts=0
+        with self.conn:
+            self.cur.executemany("INSERT INTO sl_corpus (id, segment) VALUES (?,?)",self.sl_data)   
+            self.cur.executemany("INSERT INTO tl_corpus (id, segment) VALUES (?,?)",self.tl_data)  
+        self.conn.commit()
+        
+    def load_parallel_corpus(self,parallelcorpusfile, reverse=False, encoding="utf-8"):
+        '''Loads a bilingual corpus in Moses format (that is, in two independent files. It expects one segment per line.'''
+        slcf=codecs.open(parallelcorpusfile,"r",encoding=encoding)
+        sl_data=[]
+        tl_data=[]
+        continserts=0
+        while 1:
+            segment=slcf.readline()
+            if not segment:
+                break            
+            continserts+=1
+            self.max_id_corpus+=1
+            sl_record=[]
+            tl_record=[]
+            sl_segment=segment.rstrip().split("\t")[0]
+            tl_segment=segment.rstrip().split("\t")[1]
+            
+            sl_record.append(self.max_id_corpus)
+            tl_record.append(self.max_id_corpus)
+            sl_record.append(sl_segment)
+            tl_record.append(tl_segment)
+            sl_data.append(sl_record)
+            tl_data.append(tl_record)
+            if continserts==self.maxinserts:
+                self.cur.executemany("INSERT INTO sl_corpus (id, segment) VALUES (?,?)",sl_data)
+                self.cur.executemany("INSERT INTO tl_corpus (id, segment) VALUES (?,?)",tl_data)
+                sl_data=[]
+                tl_data=[]
+                continserts=0
+        with self.conn:
+            self.cur.executemany("INSERT INTO sl_corpus (id, segment) VALUES (?,?)",sl_data)   
+            self.cur.executemany("INSERT INTO tl_corpus (id, segment) VALUES (?,?)",tl_data)  
+        self.conn.commit()
+                    
+    def load_tmx(self,tmx_file, sl_code=None, tl_code= None):
+        '''Loads a parallel corpus from a TMX file. Source and target language codes should be given. The codes must be the exactly the same as in the TMX file. In no codes are provided, the codes used in the TBXTools project will be used.'''
+        self.continserts=0
+        if sl_code==None:
+            self.sl_code=self.sl_lang
+        else:
+            self.sl_code=sl_code
+        if tl_code==None:
+            self.tl_code=self.tl_lang
+        else:
+            self.tl_code=tl_code
+        self.data1=[]
+        self.data2=[]
+        self.sl_segment=""
+        self.tl_segment=""
+        self.current_lang=""
+        for self.event, self.elem in etree.iterparse(tmx_file,events=("start","end")):
+            if self.event=='start':
+                if self.elem.tag=="tu" and not self.sl_segment=="" and not self.tl_segment=="":
+                    self.continserts+=1
+                    self.max_id_corpus+=1
+                    self.record1=[]
+                    self.record2=[]
+                    self.record1.append(self.max_id_corpus)
+                    self.record1.append(self.sl_segment)
+                    self.data1.append(self.record1)
+                    
+                    self.record2.append(self.max_id_corpus)
+                    self.record2.append(self.tl_segment)
+                    self.data2.append(self.record2)
+                    self.sl_segment=""
+                    self.tl_segment=""
+                    if self.continserts==self.maxinserts:
+                        self.cur.executemany("INSERT INTO sl_corpus (id, segment) VALUES (?,?)",self.data1) 
+                        self.cur.executemany("INSERT INTO tl_corpus (id, segment) VALUES (?,?)",self.data2)
+                        self.data1=[]
+                        self.data2=[]
+                        self.continserts=0
+                        self.conn.commit()
+                elif self.elem.tag=="tuv":
+                    self.current_lang=self.elem.attrib['{http://www.w3.org/XML/1998/namespace}lang']
+                elif self.elem.tag=="seg":
+                    if self.elem.text:
+                        self.segmentext=self.elem.text
+                    else:
+                        self.segmentext=""
+                    if self.current_lang==sl_code:
+                        self.sl_segment=self.segmentext
+                    if self.current_lang==tl_code:
+                        self.tl_segment=self.segmentext                
+                        
+
+    def load_sl_tagged_corpus(self,corpusfile,format="TBXTools",encoding="utf-8"):
+        '''Loads the source language tagged corpus. 3 formats are allowed:
+        - TBXTools: The internal format used by TBXTools. One tagged segment per line.
+                f1|l1|t1|p1 f2|l2|t2|p2 ... fn|ln|tn|pn 
+        - Freeling: One token per line and segments separated by blank lines
+                f1 l1 t1 p1
+                f2 l2 t2 p2
+                ...
+                fn ln tn pn
+        - Conll: One of the output formats guiven by the Standford Core NLP analyzer.  On token per line and segments separated by blank lines
+                id1 f1 l1 t1 ...
+                id2 f2 l2 t2 ...
+                ...
+                idn fn ln tn ...
+        '''
+        self.validformarts=["TBXTools","freeling","conll"]
+        #TODO: Raise exception if not a valid format.
+        self.cf=codecs.open(corpusfile,"r",encoding=encoding)
+        if format=="TBXTools":
+            
+            self.data=[]
+            self.continserts=0
+            for self.line in self.cf:
+                self.continserts+=1
+                self.max_id_corpus+=1
+                self.record=[]
+                self.line=self.line.rstrip()
+                self.record.append(self.max_id_corpus)
+                self.record.append(self.line)
+                self.data.append(self.record)
+                if self.continserts==self.maxinserts:
+                    self.cur.executemany("INSERT INTO sl_tagged_corpus (id, tagged_segment) VALUES (?,?)",self.data)
+                    self.data=[]
+                    self.continserts=0
+                    
+            with self.conn:
+                self.cur.executemany("INSERT INTO sl_tagged_corpus (id, tagged_segment) VALUES (?,?)",self.data)    
+            self.conn.commit()
+        elif format=="freeling":
+            self.data=[]
+            self.continserts=0
+            self.segment=[]
+            for self.line in self.cf:
+                self.line=self.line.rstrip()
+                if self.line=="":
+                    self.continserts+=1
+                    self.max_id_corpus+=1
+                    self.record=[]
+                    self.record.append(self.max_id_corpus)
+                    self.record.append(" ".join(self.segment))
+                    print(" ".join(self.segment))
+                    self.data.append(self.record)
+                    if self.continserts==self.maxinserts:
+                        self.cur.executemany("INSERT INTO sl_tagged_corpus (id, tagged_segment) VALUES (?,?)",self.data)
+                        self.data=[]
+                        self.continserts=0
+                        self.data=[]
+                        self.conn.commit()
+                    self.segment=[]
+                        
+                else:
+                    self.camps=self.line.split(" ")
+                    self.token=self.camps[0]+"|"+self.camps[1]+"|"+self.camps[2]
+
+                    self.segment.append(self.token)
+            with self.conn:
+                self.cur.executemany("INSERT INTO sl_tagged_corpus (id, tagged_segment) VALUES (?,?)",self.data)    
+                self.conn.commit()
+                    
+        
+                    
+        elif format=="conll":
+            self.data=[]
+            self.continserts=0
+            self.segment=[]
+            for self.line in self.cf:
+                self.line=self.line.rstrip()
+                if self.line=="":
+                    self.continserts+=1
+                    self.max_id_corpus+=1
+                    self.record=[]
+                    self.record.append(self.max_id_corpus)
+                    self.record.append(" ".join(self.segment))
+                    self.data.append(self.record)
+                    if self.continserts==self.maxinserts:
+                        self.cur.executemany("INSERT INTO sl_tagged_corpus (id, tagged_segment) VALUES (?,?)",self.data)
+                        self.data=[]
+                        self.continserts=0
+                        self.data=[]
+                        self.conn.commit()
+                    self.segment=[]
+                        
+                else:
+                    self.camps=self.line.split("\t")
+                    self.token=self.camps[1]+"|"+self.camps[2]+"|"+self.camps[3]
+                    self.segment.append(self.token)
+                
+                
+                
+            with self.conn:
+                self.cur.executemany("INSERT INTO sl_tagged_corpus (id, tagged_segment) VALUES (?,?)",self.data)    
+            self.conn.commit()
+            
+            
+        
+
+
+    def load_sl_stopwords(self,fitxer,encoding="utf-8"):
+        '''Loads the stopwords for the source language.'''
+        self.fc=codecs.open(fitxer,"r",encoding)
+        self.data=[]
+        self.record=[]
+        while 1:
+            self.linia=self.fc.readline()
+            if not self.linia:
+                break 
+            self.linia=self.linia.rstrip()
+            self.sl_stopwords.append(self.linia)
+            self.record.append(self.linia)
+            self.data.append(self.record)
+            self.record=[]
+        
+        for self.punct in self.punctuation:
+            self.record.append(self.punct)
+            self.data.append(self.record)
+            self.record=[]
+        with self.conn:
+            self.cur.executemany("INSERT INTO sl_stopwords (sl_stopword) VALUES (?)",self.data)  
+            
+    def load_tl_stopwords(self,fitxer,encoding="utf-8"):
+        '''Loads the stopwords for the target language.'''
+        self.fc=codecs.open(fitxer,"r",encoding)
+        self.data=[]
+        self.record=[]
+        while 1:
+            self.linia=self.fc.readline()
+            if not self.linia:
+                break 
+            self.linia=self.linia.rstrip()
+            self.tl_stopwords.append(self.linia)
+            self.record.append(self.linia)
+            self.data.append(self.record)
+            self.record=[]
+        
+        for self.punct in self.punctuation:
+            self.record.append(self.punct)
+            self.data.append(self.record)
+            self.record=[]
+        with self.conn:
+            self.cur.executemany("INSERT INTO tl_stopwords (tl_stopword) VALUES (?)",self.data) 
+
+    def load_sl_inner_stopwords(self,fitxer,encoding="utf-8"):
+        '''Loads the stopwords for the source language.'''
+        self.fc=codecs.open(fitxer,"r",encoding)
+        self.data=[]
+        self.record=[]
+        while 1:
+            self.linia=self.fc.readline()
+            if not self.linia:
+                break 
+            self.linia=self.linia.rstrip()
+            self.sl_inner_stopwords.append(self.linia)
+            self.record.append(self.linia)
+            self.data.append(self.record)
+            self.record=[]
+        
+        for self.punct in self.punctuation:
+            self.record.append(self.punct)
+            self.data.append(self.record)
+            self.record=[]
+        with self.conn:
+            self.cur.executemany("INSERT INTO sl_inner_stopwords (sl_inner_stopword) VALUES (?)",self.data)  
+            
+    def load_tl_inner_stopwords(self,fitxer,encoding="utf-8"):
+        '''Loads the inner stopwords for the target language.'''
+        self.fc=codecs.open(fitxer,"r",encoding)
+        self.data=[]
+        self.record=[]
+        while 1:
+            self.linia=self.fc.readline()
+            if not self.linia:
+                break 
+            self.linia=self.linia.rstrip()
+            self.tl_inner_stopwords.append(self.linia)
+            self.record.append(self.linia)
+            self.data.append(self.record)
+            self.record=[]
+        
+        for self.punct in self.punctuation:
+            self.record.append(self.punct)
+            self.data.append(self.record)
+            self.record=[]
+        with self.conn:
+            self.cur.executemany("INSERT INTO tl_inner_stopwords (tl_inner_stopword) VALUES (?)",self.data)  
+
+    def load_evaluation_terms(self,arxiu,encoding="utf-8",nmin=0,nmax=1000):
+        '''Loads the evaluation terms from a tabulated text'''
+        #TODO Load from TBX
+        
+        self.cf=codecs.open(arxiu,"r",encoding=encoding)
+        self.data=[]
+        self.continserts=0
+        for self.line in self.cf:
+            
+            self.line=self.line.rstrip()
+            self.continserts+=1            
+            self.record=[]
+            self.line=self.line.rstrip()
+            self.camps=self.line.split("\t")
+            if len(self.camps)==1:
+                if len(self.camps[0].split(" "))>=nmin and len(self.camps[0].split(" "))<=nmax:
+                    
+                    self.record.append(self.camps[0])
+                    self.record.append("_")
+                    self.evaluation_terms[self.camps[0]]="_"
+                    self.data.append(self.record)
+            elif len(self.camps)>1:
+                if len(self.camps[0].split(" "))>=nmin and len(self.camps[0].split(" "))<=nmax:
+                    self.record.append(self.camps[0])
+                    self.record.append(self.camps[1])
+                    self.evaluation_terms[self.camps[0]]=self.camps[1]
+                    self.data.append(self.record)
+            if self.continserts==self.maxinserts:
+                
+                self.cur.executemany("INSERT INTO evaluation_terms (sl_term,tl_term) VALUES (?,?)",self.data)
+                self.data=[]
+                self.continserts=0
+        with self.conn:
+            self.cur.executemany("INSERT INTO evaluation_terms (sl_term,tl_term) VALUES (?,?)",self.data)
+    
+        self.conn.commit()
+        
+    def load_tsr_terms(self,arxiu,encoding="utf-8",nmin=0,nmax=1000):
+        '''Loads the evaluation terms from a text file'''
+        
+        cf=codecs.open(arxiu,"r",encoding=encoding)
+        data=[]
+        continserts=0
+        for line in cf:
+            
+            line=line.rstrip()
+            continserts+=1            
+            record=[]
+            line=line.rstrip()
+            if len(line.split(" "))>=nmin and len(line.split(" "))<=nmax:
+                record.append(line)
+                #self.tsr_terms[line]="_"
+                data.append(record)
+        self.cur.executemany("INSERT INTO tsr_terms (term) VALUES (?)",data)
+        self.conn.commit()
+    
+    def load_compoundify_terms_sl(self,arxiu,encoding="utf-8",nmin=0,nmax=1000, field=0):
+        '''Loads the evaluation terms from a text file'''
+        
+        cf=codecs.open(arxiu,"r",encoding=encoding)
+        data=[]
+        continserts=0
+        for line in cf:
+            
+            line=line.rstrip()
+            camps=line.split("\t")
+            line=camps[field]
+            continserts+=1            
+            record=[]
+            line=line.rstrip()
+            if len(line.split(" "))>=nmin and len(line.split(" "))<=nmax:
+                record.append(line)
+                #self.tsr_terms[line]="_"
+                data.append(record)
+        self.cur.executemany("INSERT INTO compoundify_terms_sl (term) VALUES (?)",data)
+        self.conn.commit()
+        
+    def load_compoundify_terms_tl(self,arxiu,encoding="utf-8",nmin=0,nmax=1000, field=0):
+        '''Loads the evaluation terms from a text file'''
+        
+        cf=codecs.open(arxiu,"r",encoding=encoding)
+        data=[]
+        continserts=0
+        for line in cf:
+            line=line.rstrip()
+            camps=line.split("\t")
+            line=camps[field]
+            continserts+=1            
+            record=[]
+            line=line.rstrip()
+            if len(line.split(" "))>=nmin and len(line.split(" "))<=nmax:
+                record.append(line)
+                #self.tsr_terms[line]="_"
+                data.append(record)
+        self.cur.executemany("INSERT INTO compoundify_terms_tl (term) VALUES (?)",data)
+        self.conn.commit()
+    
+    def load_exclusion_terms(self,arxiu,encoding="utf-8",nmin=0,nmax=1000):
+        '''Loads the exclusion terms from a tabulated text. The terms in the exclusion terms will be deleted from the term candidates. It is useful to store already known terms, and/or term candidates already evaluated either as correct or as incorrect.'''
+        self.cf=codecs.open(arxiu,"r",encoding=encoding)
+        self.data=[]
+        self.continserts=0
+        for self.line in self.cf:
+            self.line=self.line.rstrip()
+            self.continserts+=1            
+            self.record=[]
+            self.line=self.line.rstrip()
+            self.camps=self.line.split("\t")
+            if len(self.camps)==1:
+                self.record.append(self.camps[0])
+                self.record.append("_")
+                self.evaluation_terms[self.camps[0]]="_"
+            elif len(self.camps)>1:
+                self.record.append(self.camps[0])
+                self.record.append(self.camps[1])
+                self.evaluation_terms[self.camps[0]]=self.camps[1]
+            self.data.append(self.record)
+            if self.continserts==self.maxinserts:
+                self.cur.executemany("INSERT INTO exclusion_terms (sl_term,tl_term) VALUES (?,?)",self.data)
+                self.data=[]
+                self.continserts=0
+        with self.conn:
+            self.cur.executemany("INSERT INTO exclusion_terms (sl_term,tl_term) VALUES (?,?)",self.data)
+    
+        self.conn.commit()
+
+    def load_sl_exclusion_regexps(self,arxiu,encoding="utf-8"):
+        '''Loads the exclusionregular expressions for the source language.'''
+        self.cf=codecs.open(arxiu,"r",encoding=encoding)
+        self.data=[]
+        for self.line in self.cf:
+            self.line=self.line.rstrip()
+            self.record=[]
+            self.record.append(self.line)
+            self.data.append(self.record)
+            
+        with self.conn:
+            self.cur.executemany('INSERT INTO sl_exclusion_regexps (sl_exclusion_regexp) VALUES (?)',self.data)
+            
+    def load_tl_exclusion_regexps(self,arxiu,encoding="utf-8"):
+        '''Loads the exclusionregular expressions for the target language.'''
+        self.cf=codecs.open(arxiu,"r",encoding=encoding)
+        self.data=[]
+        for self.line in self.cf:
+            self.line=self.line.rstrip()
+            self.record=[]
+            self.record.append(self.line)
+            self.data.append(self.record)
+            
+        with self.conn:
+            self.cur.executemany('INSERT INTO tl_exclusion_regexps (sl_exclusion_regexp) VALUES (?)',self.data)
+
+   
+    def show_term_candidates(self,limit=-1,minfreq=2, minmeasure=-1, show_frequency=True, show_measure=False, mark_eval=False, verbose=False):
+        '''Shows in screen the term candidates.'''
+        self.measure=0
+        
+        with self.conn:
+            self.cur.execute("SELECT sl_term FROM exclusion_terms")
+            for self.s in self.cur.fetchall():
+                self.knownterms.append(self.s[0])
+        with self.conn:
+            self.cur.execute("SELECT frequency,value,n,candidate FROM term_candidates order by value desc, frequency desc, random() limit "+str(limit))
+            for self.s in self.cur.fetchall():
+                self.frequency=self.s[0]
+                if self.s[1]==None:
+                    self.measure==0
+                else:
+                    self.measure=self.s[1]
+                self.n=self.s[2]
+                self.candidate=self.s[3]
+                if self.n>=self.n_min and self.n<=self.n_max and not self.candidate in self.knownterms:
+                    if mark_eval:
+                        if self.candidate in self.evaluation_terms:
+                            self.candidate="*"+self.candidate
+                    if show_frequency and not show_measure:
+                        self.cadena=str(self.frequency)+"\t"+self.candidate
+                    if not show_frequency and show_measure:
+                        self.cadena=str(self.measure)+"\t"+self.candidate
+                    if show_measure and show_frequency:
+                        self.cadena=str(self.frequency)+"\t"+str(self.measure)+"\t"+self.candidate
+                    else:
+                        self.cadena=self.candidate
+                    print(self.cadena)
+
+    def select_unigrams(self,file,position=-1,verbose=True):
+        sunigrams=codecs.open(file,"w",encoding="utf-8")
+        unigrams={}
+        self.cur.execute("SELECT frequency,candidate FROM term_candidates order by value desc, frequency desc, random()")
+        #self.cur.execute("SELECT frequency,value,n,candidate FROM term_candidates order by n desc limit "+str(limit))
+        for s in self.cur.fetchall():
+            frequency=s[0]
+            candidate=s[1].split(" ")[position]
+            if candidate in unigrams:
+                unigrams[candidate]+=frequency
+            else:
+                unigrams[candidate]=frequency
+        #for self.candidate in self.unigrams:
+        #    print(self.unigrams[self.candidate],self.candidate)
+        data=[]
+        for candidate in sorted(unigrams, key=unigrams.get, reverse=True):
+            
+            cadena=str(unigrams[candidate])+"\t"+candidate
+            #if self.verbose: print(cadena)
+            record=[]
+            record.append(candidate)
+            record.append(1)
+            record.append(unigrams[candidate])
+            record.append("freq")
+            record.append(unigrams[candidate])
+            data.append(record)
+            sunigrams.write(cadena+"\n")
+          
+        with self.conn:
+            self.cur.executemany("INSERT INTO term_candidates (candidate, n, frequency, measure, value) VALUES (?,?,?,?,?)",data)        
+            self.conn.commit()
+
+
+    def save_term_candidates(self,outfile,limit=-1,minfreq=2, minmeasure=-1, show_frequency=True, show_measure=False, mark_eval=False, verbose=False):
+        '''Saves the term candidates in a file.'''
+        self.sortida=codecs.open(outfile,"w",encoding="utf-8")
+        self.measure=0
+        self.knownterms=[]
+        with self.conn:
+            self.cur.execute("SELECT sl_term FROM exclusion_terms")
+            for self.s in self.cur.fetchall():
+                self.knownterms.append(self.s[0])
+        with self.conn:
+            self.cur.execute("SELECT frequency,value,n,candidate FROM term_candidates order by value desc, frequency desc, random() limit "+str(limit))
+            #self.cur.execute("SELECT frequency,value,n,candidate FROM term_candidates order by n desc limit "+str(limit))
+            for self.s in self.cur.fetchall():
+                self.frequency=self.s[0]
+                if self.s[1]==None:
+                    self.measure==0
+                else:
+                    self.measure=self.s[1]
+                self.n=self.s[2]
+                self.candidate=self.s[3]
+                if self.n>=self.n_min and self.n<=self.n_max and not self.candidate in self.knownterms:
+                    if mark_eval:
+                        if self.candidate in self.evaluation_terms:
+                            self.candidate="*"+self.candidate
+                    if show_measure and not show_frequency:
+                        self.cadena=str(self.measure)+"\t"+self.candidate
+                    elif show_frequency and not show_measure:
+                        self.cadena=str(self.frequency)+"\t"+self.candidate
+                    elif show_frequency and show_measure:
+                        self.cadena=str(self.frequency)+"\t"+str(self.measure)+"\t"+self.candidate
+                    else:
+                        self.cadena=self.candidate
+                    if verbose:
+                        print(self.cadena)
+                    self.sortida.write(self.cadena+"\n")
+                    
+    #STATISTICAL TERM EXTRACTION
+    
+    def ngram_calculation (self,nmin,nmax,minfreq=2):
+        '''Performs the calculation of ngrams.'''
+        ngramsFD=FreqDist()
+        tokensFD=FreqDist()
+        n_nmin=nmin
+        n_max=nmax
+            
+        with self.conn:
+            self.cur.execute('SELECT segment from sl_corpus')
+            for s in self.cur.fetchall():
+                segment=s[0]
+                for n in range(nmin,nmax+1): #we DON'T calculate one order bigger in order to detect nested candidates
+                    if self.specificSLtokenizer:
+                        tokens=self.SLtokenizer.tokenize(segment).split()
+                    else:
+                        tokens=segment.split()
+                    ngs=ngrams(tokens, n)
+                    for ng in ngs:
+                        ngramsFD[ng]+=1
+                for token in tokens:
+                    tokensFD[token]+=1
+                       
+        data=[]                
+        for c in ngramsFD.most_common():
+            if c[1]>=minfreq:
+                record=[]
+                record.append(" ".join(c[0]))            
+                record.append(len(c[0]))
+                record.append(c[1])   
+                data.append(record)
+        with self.conn:
+            self.cur.executemany("INSERT INTO ngrams (ngram, n, frequency) VALUES (?,?,?)",data) 
+            self.conn.commit()
+            
+        data=[]                
+        for c in tokensFD.most_common():
+            record=[]
+            record.append(c[0])            
+            record.append(c[1])   
+            data.append(record)
+        with self.conn:
+            self.cur.executemany("INSERT INTO tokens (token, frequency) VALUES (?,?)",data) 
+            self.conn.commit()
+            
+    def statistical_term_extraction(self,minfreq=2):
+        '''Performs an statistical term extraction using the extracted ngrams (ngram_calculation should be executed first) Loading stop-words is advisable. '''
+        self.cur.execute("DELETE FROM term_candidates")
+        self.conn.commit()
+        self.cur.execute("SELECT ngram, n, frequency FROM ngrams order by frequency desc")
+        self.results=self.cur.fetchall()
+        self.data=[] 
+        for self.a in self.results:
+            self.ng=self.a[0].split(" ")
+            self.include=True
+            if self.ng[0].lower() in self.sl_stopwords: self.include=False
+            if self.ng[-1].lower() in self.sl_stopwords: self.include=False
+            for self.i in range(1,len(self.ng)):
+                if self.ng[self.i].lower() in self.sl_inner_stopwords:
+                    self.include=False
+            if self.include:
+                self.record=[]
+                self.record.append(self.a[0])            
+                self.record.append(self.a[1])
+                self.record.append(self.a[2])
+                self.record.append("freq")
+                self.record.append(self.a[2])   
+                self.data.append(self.record)
+            if self.a[2]<minfreq:
+                break
+        with self.conn:
+            self.cur.executemany("INSERT INTO term_candidates (candidate, n, frequency, measure, value) VALUES (?,?,?,?,?)",self.data)        
+            self.conn.commit()
+    def loadSLTokenizer(self, tokenizer):
+        if not tokenizer.endswith(".py"): tokenizer=tokenizer+".py"
+        spec = importlib.util.spec_from_file_location('', tokenizer)
+        tokenizermod = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(tokenizermod)
+        self.SLtokenizer=tokenizermod.Tokenizer()
+        self.specificSLtokenizer=True
+        
+    def unloadSLTokenizer(self):
+        self.SLtokenizer=None
+        self.specificSLtokenizer=False
+        
+    def loadTLTokenizer(self, tokenizer):
+        if not tokenizer.endswith(".py"): tokenizer=tokenizer+".py"
+        spec = importlib.util.spec_from_file_location('', tokenizer)
+        tokenizermod = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(tokenizermod)
+        self.TLtokenizer=tokenizermod.Tokenizer()
+        self.specificTLtokenizer=True
+        
+    def unloadSLTokenizer(self):
+        self.TLtokenizer=None
+        self.specificTLtokenizer=False
+            
+    def statistical_term_extraction_by_segment(self, segment, minlocalfreq=1, minglobalfreq=2, maxcandidates=2, nmin=1, nmax=4):
+        '''Performs an statistical term extraction over a single segment using the extracted ngrams (ngram_calculation should be executed first) Loading stop-words is advisable. '''
+        ngramsFD=FreqDist()
+        for n in range(nmin,nmax+1): #we DON'T calculate one order bigger in order to detect nested candidates
+            if self.specificSLtokenizer:
+                tokens=self.SLtokenizer.tokenize(segment).split(" ")
+            else:
+                tokens=segment.split()
+            ngs=ngrams(tokens, n)
+            for ng in ngs:
+                include=True
+                print(ng)
+                if ng[0].lower() in self.sl_stopwords: include=False
+                if ng[-1].lower() in self.sl_stopwords: include=False
+                for i in range(1,len(ng)):
+                    if ng[i].lower() in self.sl_inner_stopwords:
+                        include=False
+                if include: ngramsFD[" ".join(ng)]+=1
+                
+        for ng in ngramsFD.most_common():
+            print(ng)
+
+    def case_normalization(self,verbose=False):
+        '''
+        Performs case normalization. If a capitalized term exists as non-capitalized, the capitalized one will be deleted and the frequency of the non-capitalized one will be increased by the frequency of the capitalized.
+        '''
+        self.cur.execute("SELECT candidate,frequency FROM term_candidates order by frequency desc")
+        self.results=self.cur.fetchall()
+        self.auxiliar={}
+        for self.r in self.results:
+            self.auxiliar[self.r[0]]=self.r[1]
+        for self.a in self.results:
+            if not self.a[0]==self.a[0].lower() and self.a[0].lower() in self.auxiliar:
+                #self.term_candidates.inc(c[0].lower(),self.term_candidates[c[0]])
+                #self.term_candidates[c[0].lower()]+=self.term_candidates[c[0]]
+                #self.term_candidates.pop(c[0])     
+                self.terma=self.a[0]
+                self.termb=self.a[0].lower()
+                self.freqa=self.a[1]
+                self.freqb=self.auxiliar[self.termb]
+                
+                self.n=len(self.termb.split(" "))
+                self.freqtotal=self.freqa+self.freqb
+                if verbose:
+                    print(self.terma,self.freqa,"-->",self.termb,self.freqb,"-->",self.freqtotal)
+                self.cur.execute('DELETE FROM term_candidates WHERE candidate=?', (self.terma,))
+                self.cur.execute('DELETE FROM term_candidates WHERE candidate=?', (self.termb,))
+                #self.cur.execute("INSERT INTO term_candidates (candidate, n, frequency) VALUES (?,?,?)",(self.termb,self.n,self.freqtotal,))
+                self.cur.execute("INSERT INTO term_candidates (candidate, n, frequency, measure, value) VALUES (?,?,?,?,?)",(self.termb,self.n,self.freqtotal,"freq",self.freqtotal))
+        self.conn.commit()
+
+    def nest_normalization(self,percent=10,verbose=False):
+        '''
+        Performs a normalization of nested term candidates. If an n-gram candidate A is contained in a n+1 candidate B and freq(A)==freq(B) or they are close values (determined by the percent parameter, A is deleted B remains as it is)
+        '''
+        self.cur.execute("SELECT candidate,frequency,n FROM term_candidates order by frequency desc")
+        self.results=self.cur.fetchall()
+        for self.a in self.results:
+            
+            self.ta=self.a[0]
+            self.fa=self.a[1]
+            self.na=self.a[2]
+            self.nb=self.na+1
+            self.fmax=self.fa+self.fa*percent/100
+            self.fmin=self.fa-self.fa*percent/100
+            self.cur.execute("SELECT candidate,frequency FROM term_candidates where frequency <="+str(self.fmax)+" and frequency>="+str(self.fmin)+"  and n ="+str(self.nb)+ " and n<="+str(self.n_max))
+            self.results2=self.cur.fetchall()
+            for self.b in self.results2:
+                self.tb=self.b[0]
+                self.fb=self.b[1]
+                #if abs(self.fa-self.fb)<=max(self.fa,self.fb)/percent:
+                if not self.ta==self.tb and not self.tb.find(self.ta)==-1: 
+                
+                    self.cur.execute('DELETE FROM term_candidates WHERE candidate=?', (self.ta,))
+                    if verbose:
+                        print(str(self.fa),self.ta,"-->",str(self.fb),self.tb)
+
+        self.conn.commit()
+
+    def regexp_exclusion(self,verbose=False):
+        '''Deletes term candidates matching a set of regular expresions loaded with the load_sl_exclusion_regexps method.'''
+        self.cur.execute("SELECT sl_exclusion_regexp FROM sl_exclusion_regexps")
+        self.results=self.cur.fetchall()
+        for self.r in self.results:
+            self.nregexp=len(self.r[0].split(" "))
+            self.exreg=self.r[0]
+            self.cur.execute("SELECT candidate FROM term_candidates where n<="+str(self.n_max))
+            self.results=self.cur.fetchall()
+            self.cexreg=re.compile(self.exreg)
+            for self.a in self.results:
+                self.candidate=self.a[0]
+                self.ncandidate=len(self.candidate.split(" "))
+                self.match=re.match(self.cexreg,self.candidate)
+                #self.match=re.match(r'\W+$',self.candidate)
+                
+                if not self.match==None and self.nregexp==self.ncandidate:
+                    self.cur.execute('DELETE FROM term_candidates WHERE candidate=?', (self.candidate,))
+                    if verbose:
+                        print(self.exreg,"-->",self.candidate)
+            self.conn.commit()
+
+    #EVALUATION
+    
+    
+     
+    def evaluate_pos(self,limit,order="desc",iterations=1000,ignore_case=True):
+        '''Performs the evaluation of the term candidates using the evaluation_terms loaded with the load_evaluation_terms method.'''
+        self.correct=0
+        self.total=0
+        self.evaluation_terms=[]
+        self.cur.execute("SELECT sl_term FROM evaluation_terms")
+        results=self.cur.fetchall()
+        for r in results:
+            self.evaluation_terms.append(r[0])
+        self.tsr_terms=[]
+        self.cur.execute("SELECT term FROM tsr_terms")
+        results=self.cur.fetchall()
+        for r in results:
+            self.tsr_terms.append(r[0])
+        self.evaluation_terms.extend(self.tsr_terms)
+        with self.conn:
+            for i in range(0,iterations):
+                if order=="desc":
+                    self.cur.execute("SELECT candidate,value from term_candidates where n<="+str(self.n_max)+" order by value desc, frequency desc, random() limit "+str(limit))
+                elif order=="asc":
+                    self.cur.execute("SELECT candidate from term_candidates where n<="+str(self.n_max)+" order by value asc, frequency desc, random() limit "+str(limit))
+                else:
+                    raise NameError('Order must be desc (decending) or asc (ascending). Defaulf value: desc')
+                #self.cur.execute("SELECT candidate from term_candidates order by id limit "+str(limit))
+                for self.s in self.cur.fetchall():
+                    self.total+=1
+                    self.candidate=self.s[0]
+                    if ignore_case:
+                        if self.candidate in self.evaluation_terms:
+                            self.correct+=1
+                        elif self.candidate.lower() in self.evaluation_terms:
+                            self.correct+=1
+                    else:
+                        if self.candidate in self.evaluation_terms:
+                            self.correct+=1
+            self.correct=self.correct/iterations
+            self.total=self.total/iterations
+            
+        try:
+            self.precisio=100*self.correct/self.total
+            self.recall=100*self.correct/len(self.evaluation_terms)
+            self.f1=2*self.precisio*self.recall/(self.precisio+self.recall)
+            return(limit,self.correct,self.total,self.precisio,self.recall,self.f1)
+        except:
+            return(limit,0,0,0,0,0)
+
+    def association_measures(self,measure="raw_freq"):
+        measurename=measure
+        bigram_measures = myBigramAssocMeasures()
+        trigram_measures = myTrigramAssocMeasures()
+        quadgram_measures = myQuadgramAssocMeasures()
+        
+        fd_tokens=nltk.FreqDist()
+        fd_bigrams=nltk.FreqDist()
+        fd_trigrams=nltk.FreqDist()
+        fd_quadgrams=nltk.FreqDist()
+        wildcard_fd=nltk.FreqDist()
+        self.cur.execute("SELECT token,frequency from tokens")
+        for s in self.cur.fetchall():
+            aux=(s[0])
+            fd_tokens[aux]+=s[1]
+            
+        textcorpus=[]
+        self.cur.execute("SELECT segment from sl_corpus")
+        for segment in self.cur.fetchall():
+            textcorpus.extend(segment[0].split(" "))
+            
+        bigram_finder=BigramCollocationFinder.from_words(textcorpus)
+        trigram_finder=TrigramCollocationFinder.from_words(textcorpus)
+        quadgram_finder=QuadgramCollocationFinder.from_words(textcorpus)
+        
+        self.cur.execute("SELECT ngram,frequency,n from ngrams")
+        results=self.cur.fetchall()
+        for r in results:
+            data=[]
+            data.append(r[0])
+            self.cur2.execute("UPDATE term_candidates SET value=NULL where candidate=?",data)
+        self.conn.commit()
+        data=[]
+        bigram_measure=[]
+        try:
+            bigram_measure=eval("bigram_finder.score_ngrams(bigram_measures."+measure+")")
+        except:
+            print("ERROR: measure "+measure+ " not implemented for bigrams",sys.exc_info())
+            #sys.exit()
+            
+        for nose in bigram_measure:
+            record=[]
+            term_candidate=" ".join(nose[0])
+            mvalue=nose[1]
+            record.append(measure)
+            record.append(mvalue)
+            record.append(term_candidate)
+            data.append(record)
+        
+        trigram_measure=[]        
+        try:
+            trigram_measure=eval("trigram_finder.score_ngrams(trigram_measures."+measure+")")
+        except:
+            print("ERROR: measure "+measure+ " not implemented for trigrams")
+            #sys.exit()
+        for nose in trigram_measure:
+            record=[]
+            term_candidate=" ".join(nose[0])
+            mvalue=nose[1]
+            record.append(measure)
+            record.append(mvalue)
+            record.append(term_candidate)
+            data.append(record)
+        quadgram_measure=[]  
+        try:
+            quadgram_measure=eval("quadgram_finder.score_ngrams(quadgram_measures."+measure+")")
+        except:
+            print("ERROR: measure "+measure+ " not implemented for quadgrams")
+            #sys.exit()
+        
+        for nose in quadgram_measure:
+            record=[]
+            term_candidate=" ".join(nose[0])
+            mvalue=nose[1]
+            record.append(measure)
+            record.append(mvalue)
+            record.append(term_candidate)
+            data.append(record)
+            
+        self.conn.executemany("UPDATE term_candidates SET measure=?,value=? where candidate=?",data)
+        self.conn.commit()
+    
+
+
+    def index_phrase_table(self,phrasetable):
+        '''Indexes a phrase table from Moses.'''
+        self.entrada=gzip.open(phrasetable, mode='rt',encoding='utf-8')
+
+        self.pt={}
+        self.continserts=0
+        self.record=[]
+        self.data=[]
+        while 1:
+            self.linia=self.entrada.readline()
+            if not self.linia:
+                break
+            self.linia=self.linia.rstrip()
+            self.camps=self.linia.split(" ||| ")
+            self.source=self.camps[0].strip()
+            self.trad=self.camps[1].strip()
+            self.probs=self.camps[2].split(" ")
+            try:
+                if not self.trad[0] in self.punctuation and not self.source[0] in self.punctuation and not self.trad[-1] in self.punctuation and not self.source[-1] in self.punctuation:
+                    #Currently, four different phrase translation scores are computed:
+                    #0    inverse phrase translation probability φ(f|e)
+                    #1    inverse lexical weighting lex(f|e)
+                    #2    direct phrase translation probability φ(e|f)
+                    #3    direct lexical weighting lex(e|f)
+                    #self.probtrad=float(self.probs[1])
+                    self.probtrad=(float(self.probs[2])*float(self.probs[3]))
+                    #print(self.source,self.trad,self.probtrad)
+                    self.record=[]
+                    self.record.append(self.source)
+                    self.record.append(self.trad)
+                    self.record.append(self.probtrad)
+                    self.data.append(self.record)
+                    self.continserts+=1
+                    if self.continserts==self.maxinserts:
+                        self.cur.executemany("INSERT INTO index_pt (source, target, probability) VALUES (?,?,?)",self.data)
+                        self.data=[]
+                        self.continserts=0
+                        self.conn.commit()
+            except:
+                pass
+        with self.conn:
+            self.cur.executemany("INSERT INTO index_pt (source, target, probability) VALUES (?,?,?)",self.data)    
+        self.conn.commit()
+                
+        
+    def find_translation_ptable(self,sourceterm,maxdec=1,maxinc=1,ncandidates=5,separator=":"):
+        '''Finds translation equivalents in an indexed phrase table table. Requires an indexed phrase table and a a list of terms separated by ":".
+        The number of translation candidates can be fixed, as well as the maximum decrement and increment of the number of tokens of the translation candidate'''
+        #select target from index_pt where source="international conflict";
+        self.cur.execute('SELECT target,probability FROM index_pt where source =?',(sourceterm,))
+        self.results=self.cur.fetchall()
+        self.targetcandidates={}
+        for self.a in self.results:
+            self.targetterm=self.a[0]
+            self.probability=float(self.a[1])
+            self.tttokens=self.targetterm.split(" ")
+            
+            if not self.tttokens[0] in self.tl_stopwords and not self.tttokens[-1] in self.tl_stopwords and len(self.tttokens)>=len(sourceterm.split(" "))-maxdec and len(self.tttokens)<=len(sourceterm.split(" "))+maxinc:
+                self.targetcandidates[self.targetterm]=self.probability
+        self.sorted_x = sorted(self.targetcandidates.items(), key=operator.itemgetter(1),reverse=True)
+        self.results=[]
+        for self.s in self.sorted_x:
+            self.results.append(self.s[0].replace(":",";"))
+        return(separator.join(self.results[0:ncandidates]))
+        
+                
+   
+    def start_freeling_api(self,freelingpath, LANG):
+        
+        if not freelingpath.endswith("/"):freelingpath=freelingpath+"/"
+        try:
+            sys.path.append(freelingpath+"APIs/python3/")
+            import pyfreeling
+        except:
+            #pass
+            print("No Freeling API available. Verify Freeling PATH: "+freelingpath+"freeling/APIs/python3/")
+        
+        pyfreeling.util_init_locale("default");
+
+        # create language analyzer
+        self.la1=pyfreeling.lang_ident(freelingpath+"common/lang_ident/ident.dat");
+
+        # create options set for maco analyzer. Default values are Ok, except for data files.
+        self.op1= pyfreeling.maco_options(LANG);
+        self.op1.set_data_files( "", 
+                           freelingpath + "common/punct.dat",
+                           freelingpath+ LANG + "/dicc.src",
+                           freelingpath + LANG + "/afixos.dat",
+                           "",
+                           freelingpath + LANG + "/locucions.dat", 
+                           freelingpath + LANG + "/np.dat",
+                           freelingpath + LANG + "/quantities.dat",
+                           freelingpath + LANG + "/probabilitats.dat");
+
+        # create analyzers
+        self.tk1=pyfreeling.tokenizer(freelingpath+LANG+"/tokenizer.dat");
+        self.sp1=pyfreeling.splitter(freelingpath+LANG+"/splitter.dat");
+        self.sid1=self.sp1.open_session();
+        self.mf1=pyfreeling.maco(self.op1);
+
+        # activate mmorpho odules to be used in next call
+        #(self, umap: "bool", num: "bool", pun: "bool", dat: "bool",
+        # dic: "bool", aff: "bool", comp: "bool", rtk: "bool", 
+        # mw: "bool", ner: "bool", qt: "bool", prb: "bool")
+        #deactivate mw
+        self.mf1.set_active_options(False, True, True, False,  # select which among created 
+                              True, True, False, True,  # submodules are to be used. 
+                              False, False, True, True ); # default: all created submodules are used
+
+        # create tagger, sense anotator, and parsers
+        self.tg1=pyfreeling.hmm_tagger(freelingpath+LANG+"/tagger.dat",True,2);
+        
+    def tag_freeling_api(self,corpus="source"):
+        with self.conn:
+            self.data=[]
+            if corpus=="source":
+                self.cur.execute('SELECT id,segment from sl_corpus')
+            elif corpus=="target":
+                self.cur.execute('SELECT id,segment from tl_corpus')
+            for self.s in self.cur.fetchall():
+                self.id=self.s[0]
+                self.segment=self.s[1]
+                self.l1 = self.tk1.tokenize(self.segment);
+                self.ls1 = self.sp1.split(self.sid1,self.l1,True);
+                self.ls1 = self.mf1.analyze(self.ls1);
+                self.ls1 = self.tg1.analyze(self.ls1);
+                self.ttsentence=[]
+                for self.s in self.ls1 :
+                  self.ws = self.s.get_words();
+                  for self.w in self.ws :
+                    self.form=self.w.get_form()
+                    self.lemma=self.w.get_lemma()
+                    self.tag=self.w.get_tag()
+                    self.ttsentence.append(self.form+"|"+self.lemma+"|"+self.tag)
+                self.ttsentence=" ".join(self.ttsentence)
+                self.record=[]
+                self.record.append(self.id)
+                self.record.append(self.ttsentence)
+                self.data.append(self.record)
+                if self.continserts==self.maxinserts:
+                    if corpus=="source":
+                        self.cur.executemany("INSERT INTO sl_tagged_corpus (id, tagged_segment) VALUES (?,?)",self.data)
+                    if corpus=="target":
+                        self.cur.executemany("INSERT INTO tl_tagged_corpus (id, tagged_segment) VALUES (?,?)",self.data)
+                    self.data=[]
+                    self.continserts=0
+            with self.conn:
+                if corpus=="source":
+                    self.cur.executemany("INSERT INTO sl_tagged_corpus (id, tagged_segment) VALUES (?,?)",self.data) 
+                if corpus=="target":
+                    self.cur.executemany("INSERT INTO tl_tagged_corpus (id, tagged_segment) VALUES (?,?)",self.data)
+    def save_sl_tagged_corpus(self,outputfile,encoding="utf-8"):
+        self.sortida=codecs.open(outputfile,"w",encoding=encoding)
+        self.cur.execute('SELECT tagged_segment from sl_tagged_corpus')
+        for self.s in self.cur.fetchall():
+            self.tagged_segment=self.s[0]
+            self.sortida.write(self.tagged_segment+"\n")
+
+    
+    def tagged_ngram_calculation (self,nmin=2,nmax=3,minfreq=2):
+        '''Calculates the tagged ngrams.'''
+        self.ngrams=FreqDist()
+        self.n_nmin=nmin
+        self.n_max=nmax
+        with self.conn:
+            self.data=[]
+            self.record=[]
+            self.record.append(self.n_min)
+            self.data.append(self.record)
+            #self.conn.executemany("UPDATE configuration SET n_min=? where id=0",self.data)
+            self.data=[]
+            self.record=[]
+            self.record.append(self.n_max)
+            self.data.append(self.record)
+            #self.conn.executemany("UPDATE configuration SET n_max=? where id=0",self.data)
+            #cur.execute("UPDATE Contacts SET FirstName = ? WHERE LastName = ?", (Fname, Lname))
+
+            self.conn.commit()
+            
+        with self.conn:
+            self.cur.execute('SELECT tagged_segment from sl_tagged_corpus')
+            
+            for self.s in self.cur.fetchall():
+                self.segment=self.s[0]
+                
+                for self.n in range(nmin,nmax+1):
+                    self.ngs=ngrams(self.segment.split(), self.n)
+                    for self.ng in self.ngs:
+                        self.ngrams[self.ng]+=1
+                        
+        self.data=[]                
+        for self.c in self.ngrams.most_common():
+           if self.c[1]>=minfreq:
+                self.candidate=[]
+                for self.ngt in self.c[0]:
+                    self.candidate.append(self.ngt.split("|")[0])
+                self.candidate=" ".join(self.candidate)
+                self.record=[]
+                self.record.append(self.candidate)
+                self.record.append(" ".join(self.c[0])) 
+                self.record.append(len(self.c[0]))
+                self.record.append(self.c[1])   
+                self.data.append(self.record)
+        with self.conn:
+            self.cur.executemany("INSERT INTO tagged_ngrams (ngram, tagged_ngram, n, frequency) VALUES (?,?,?,?)",self.data) 
+            self.conn.commit()
+                    
+    def translate_linguistic_pattern(self,pattern):
+           
+        self.aux=[]
+        for self.ptoken in pattern.split(" "):
+            self.auxtoken=[]
+            self.ptoken=self.ptoken.replace(".*","[^\s]+") #ATENCIÓ AIXÓ ÉS NOU, VERIFICAR SI CAL
+            for self.pelement in self.ptoken.split("|"):
+                if self.pelement=="#":
+                    self.auxtoken.append("([^\s]+?)")                    
+                elif self.pelement=="":
+                    self.auxtoken.append("[^\s]+?")
+                else:
+                    if self.pelement.startswith("#"):
+                        self.auxtoken.append("("+self.pelement.replace("#","")+")")
+                    else:
+                        self.auxtoken.append(self.pelement)
+            self.aux.append("\|".join(self.auxtoken))
+        self.tp="("+" ".join(self.aux)+")"
+        #self.tpatterns.append(self.tp)
+        return(self.tp)       
+    
+    def load_linguistic_patterns(self,file, encoding="utf-8"):
+        '''Loads the linguistic patterns to use with linguistic terminology extraction.'''
+        self.entrada=codecs.open(file,"r",encoding=encoding)
+        self.linguistic_patterns=[]
+        self.data=[]
+        self.record=[]
+        for self.linia in self.entrada:
+            self.linia=self.linia.rstrip()
+            self.pattern=self.translate_linguistic_pattern(self.linia)
+            self.record.append(self.pattern)
+            self.data.append(self.record)
+            self.record=[]
+        with self.conn:
+            self.cur.executemany("INSERT INTO linguistic_patterns (linguistic_pattern) VALUES (?)",self.data)
+        
+         
+
+    def linguistic_term_extraction(self,minfreq=2):
+        '''Performs an linguistic term extraction using the extracted tagged ngrams (tagged_ngram_calculation should be executed first). '''
+        self.linguistics_patterns=[]
+        self.controlpatterns=[]
+        with self.conn:
+            self.cur.execute("SELECT linguistic_pattern from linguistic_patterns")
+            for self.lp in self.cur.fetchall():
+                self.linguistic_pattern=self.lp[0]
+                self.transformedpattern="^"+self.linguistic_pattern+"$"
+                if not self.transformedpattern in self.controlpatterns:
+                    self.linguistic_patterns.append(self.transformedpattern)
+                    self.controlpatterns.append(self.transformedpattern)
+                    
+            
+        self.cur.execute("SELECT tagged_ngram, n, frequency FROM tagged_ngrams order by frequency desc")
+        self.results=self.cur.fetchall()
+        self.data=[] 
+        
+        for self.a in self.results:
+            self.include=True
+            self.ng=self.a[0]
+            self.n=self.a[1]
+            self.frequency=self.a[2]
+            try:
+                if self.ng.split(" ")[0].split("|")[1].lower() in self.sl_stopwords: self.include=False
+            except:
+                pass
+            try:
+                if self.ng.split(" ")[-1].split("|")[1].lower() in self.sl_stopwords: self.include=False
+            except:
+                pass
+            if self.frequency<minfreq:
+                break
+            if self.include:
+                for self.pattern in self.linguistic_patterns:
+                    self.match=re.search(self.pattern,self.ng)
+                    if self.match:
+                        if self.match.group(0)==self.ng:          
+                            self.candidate=" ".join(self.match.groups()[1:])
+                            self.record=[]
+                            self.record.append(self.candidate)     
+                            self.record.append(self.n)
+                            self.record.append(self.frequency)   
+                            self.record.append("freq")
+                            self.record.append(self.frequency)   
+                            self.data.append(self.record)
+                            break
+                
+        with self.conn:
+            #self.cur.executemany("INSERT INTO term_candidates (candidate, n, frequency) VALUES (?,?,?)",self.data)  
+            self.cur.executemany("INSERT INTO term_candidates (candidate, n, frequency, measure, value) VALUES (?,?,?,?,?)",self.data)      
+            self.conn.commit()
+            
+        #eliminem candidats repetits
+        self.cur.execute("SELECT candidate, n, frequency FROM term_candidates")
+        self.results=self.cur.fetchall()
+        self.tcaux={}
+        for self.a in self.results:
+            if not self.a[0] in self.tcaux:
+                self.tcaux[self.a[0]]=self.a[2]
+            else:
+                self.tcaux[self.a[0]]+=self.a[2]
+        
+        self.cur.execute("DELETE FROM term_candidates")
+        self.conn.commit()
+        self.data=[] 
+        for self.tc in self.tcaux:
+            self.record=[]
+            self.record.append(self.tc)            
+            self.record.append(len(self.tc.split(" ")))
+            self.record.append(self.tcaux[self.tc])   
+            self.record.append("freq")
+            self.record.append(self.tcaux[self.tc])   
+            self.data.append(self.record)
+        with self.conn:
+            #self.cur.executemany("INSERT INTO term_candidates (candidate, n, frequency) VALUES (?,?,?)",self.data)        
+            self.cur.executemany("INSERT INTO term_candidates (candidate, n, frequency, measure, value) VALUES (?,?,?,?,?)",self.data) 
+            self.conn.commit()
+            
+     
+    def learn_linguistic_patterns(self,outputfile,showfrequencies=False,encoding="utf-8",verbose=True,representativity=100):
+        self.learntpatterns={}
+        self.sortida=codecs.open(outputfile,"w",encoding=encoding)
+        self.acufreq=0
+        tags={}
+        with self.conn:
+            self.cur.execute("SELECT sl_term FROM evaluation_terms")
+            for self.s in self.cur.fetchall():
+                self.cur.execute("SELECT tagged_ngram, n, frequency FROM tagged_ngrams WHERE ngram= ?", (self.s[0],))
+                self.results=self.cur.fetchall()
+                if len(self.results)>0:
+                    for self.a in self.results:
+                        self.ng=self.a[0]
+                        nglist=self.ng.split()
+                        self.n=self.a[1]
+                        self.frequency=self.a[2]
+                        self.candidate=[]
+                        self.ngtokenstag=self.ng.split(" ")
+                        for self.ngt in self.ngtokenstag:
+                            self.candidate.append(self.ngt.split("|")[0])
+                        self.candidate=" ".join(self.candidate)
+                        self.t2=self.ng.split(" ")
+                        self.t1=self.candidate.split(" ")
+                        self.patternbrut=[]
+                        for self.position in range(0,self.n):
+                            self.t2f=self.t2[self.position].split("|")[0]
+                            self.t2l=self.t2[self.position].split("|")[1]
+                            self.t2t=self.t2[self.position].split("|")[2]
+                            self.patternpart=""
+                            if self.t1[self.position]==self.t2l:
+                                self.patternpart="|#|"+self.t2t
+                            elif self.t1[self.position]==self.t2f:
+                                self.patternpart="#||"+self.t2t
+                            self.patternbrut.append(self.patternpart)
+                        self.pattern=" ".join(self.patternbrut)
+                        if self.pattern in self.learntpatterns:
+                            self.learntpatterns[self.pattern]+=self.n
+                            self.acufreq+=self.n
+                        else:
+                            self.learntpatterns[self.pattern]=self.n
+                            self.acufreq+=self.n
+        self.sorted_x = sorted(self.learntpatterns.items(), key=operator.itemgetter(1),reverse=True)
+        self.results=[]
+        self.acufreq2=0
+        for self.s in self.sorted_x:
+            self.percent=100*self.acufreq2/self.acufreq
+            if self.percent>representativity:
+                break
+            self.acufreq2+=self.s[1]
+            if showfrequencies:
+                cadena=str(self.s[1])+"\t"+self.s[0]
+            else:
+                cadena=self.s[0]
+            self.sortida.write(cadena+"\n")
+            if verbose:
+                print(cadena)
+                                
+    def find_translation_pcorpus(self,slterm,maxdec=1,maxinc=1,ncandidates=5,separator=":"):
+        self.nmin=len(slterm.split(" "))-maxdec
+        self.nmax=len(slterm.split(" "))+maxinc
+        self.tlngrams=FreqDist()
+        with self.conn:
+            self.cur.execute('SELECT id, segment from sl_corpus')
+            
+            for self.s in self.cur.fetchall():
+                self.segment=self.s[1]
+                self.id=self.s[0]
+                
+                if self.segment.find(slterm)>-1:
+                    self.cur2.execute('SELECT segment from tl_corpus where id="'+str(self.id)+'"')
+                    for self.s2 in self.cur2.fetchall():
+                        self.tl_segment=self.s2[0]
+                        for self.n in range(self.nmin,self.nmax+1):
+                            #self.tlngs=ngrams(self.tl_tokenizer.tokenize(self.tl_segment), self.n)
+                            self.tlngs=ngrams(self.tl_segment.split(" "), self.n)
+                            for self.tlng in self.tlngs:
+                                if not self.tlng[0] in self.tl_stopwords and not self.tlng[-1] in self.tl_stopwords:
+                                    self.tlngrams[self.tlng]+=1
+            self.resultlist=[]
+            for self.c in self.tlngrams.most_common(ncandidates):
+                self.resultlist.append(" ".join(self.c[0]))
+            
+            return(separator.join(self.resultlist))
+
+#EMBEDDINGS
+
+    def calculate_embeddings_sl(self,filename,vector_size=300, window=5, min_count=1, workers=4):
+        self.cur.execute('SELECT id, segment from sl_corpus')
+        data = []
+        for s in self.cur.fetchall():
+            temp=[]
+            segment=s[1] 
+            if self.specificSLtokenizer:
+                tokens=self.SLtokenizer.tokenize(segment).split(" ")
+            else:
+                tokens=segment.split()
+            data.append(tokens)
+        model = Word2Vec(sentences=data, vector_size=vector_size, window=window, min_count=min_count, workers=workers)
+        model.wv.save_word2vec_format(filename, binary=False)
+     
+    def calculate_embeddings_sl_ref(self,filename,vector_size=300, window=5, min_count=1, workers=4):
+        self.cur.execute('SELECT id, segment from tl_corpus')
+        data = []
+        for s in self.cur.fetchall():
+            temp=[]
+            segment=s[1] 
+            if self.specificSLtokenizer:
+                tokens=self.SLtokenizer.tokenize(segment).split(" ")                
+            else:
+                tokens=segment.split()
+            data.append(tokens)
+        model = Word2Vec(sentences=data, vector_size=vector_size, window=window, min_count=min_count, workers=workers)
+        model.wv.save_word2vec_format(filename, binary=False)
+    
+    def calculate_embeddings_tl(self,filename,vector_size=300, window=5, min_count=1, workers=4):
+        self.cur.execute('SELECT id, segment from tl_corpus')
+        data = []
+        for s in self.cur.fetchall():
+            temp=[]
+            segment=s[1] 
+            if self.specificTLtokenizer:
+                tokens=self.TLtokenizer.tokenize(segment).split(" ")
+            else:
+                tokens=segment.split()
+            data.append(tokens)
+        model = Word2Vec(sentences=data, vector_size=vector_size, window=window, min_count=min_count, workers=workers)
+        model.wv.save_word2vec_format(filename, binary=False)
+    
+    def mapEmbeddings(self,src_input,trg_input,src_output,trg_output,init_dictionary):
+        map_embeddings.supervised_mapping(src_input,trg_input,src_output,trg_output,init_dictionary)
+        
+    def load_SL_embeddings(self, file, binary=False):
+        self.wvSL = KeyedVectors.load_word2vec_format(file, binary=False)
+        
+    def load_TL_embeddings(self, file, binary=False):
+        self.wvTL = KeyedVectors.load_word2vec_format(file, binary=False)
+        
+    
+    def find_translation_wv(self, term, ncandidates=10):
+        term=term.strip().replace(" ","▁")
+        try:
+            vector=self.wvSL[term]
+            tcandidates = self.wvTL.most_similar([vector], topn=ncandidates)
+        except:
+            tcandidates=[]
+        resposta=[]
+        for tc in tcandidates:
+            tc=tc[0].replace("▁"," ")
+            resposta.append(tc)
+        return(resposta)
+        
+
+
+#TSR
+    def tsr(self, type="combined",max_iterations=10000000000, verbose=True): 
+        component={}
+        firstcomponent={}
+        middlecomponent={}
+        lastcomponent={}
+        self.tsr_terms=[]
+        self.cur.execute("SELECT term FROM tsr_terms")
+        results=self.cur.fetchall()
+        for r in results:
+            self.tsr_terms.append(r[0])
+        for term in self.tsr_terms:
+            camps=term.split(" ")
+            if len(camps)==1: #UNIGRAMS
+                firstcomponent[camps[0].lower()]=1
+                lastcomponent[camps[0].lower()]=1
+            if len(camps)>=2:
+                firstcomponent[camps[0].lower()]=1
+                lastcomponent[camps[-1].lower()]=1
+                component[camps[0].lower()]=1
+                component[camps[-1].lower()]=1
+                if len(camps)>=3:
+                    for i in range(1,len(camps)-1):
+                        middlecomponent[camps[i].lower()]=1
+                        component[camps[i].lower()]=1
+
+        new=True
+        newcandidates={} #candidate-frequency
+        hashmeasure={}
+        hashvalue={}
+        
+        newcandidatestempstric={} #candidate-frequency                                                                    
+        hashmeasuretempstrict={}
+        hashvaluetempstric={}
+
+        newcandidatestempflexible={} #candidate-frequency
+        hashmeasuretempflexible={}
+        hashvaluetempflexible={}
+        
+        newcandidatestempcombined={} #candidate-frequency
+        hashmeasuretempcombined={}
+        hashvaluetempcombined={}
+        
+        iterations=0
+        while new:
+            iterations+=1
+            if verbose: print("ITERATION",iterations)
+            new=False
+            self.cur.execute("SELECT candidate,n,frequency,measure,value FROM term_candidates ")
+            results=self.cur.fetchall()
+            auxiliar={}
+            value=max_iterations-iterations#r[4]
+            for r in results:
+                candidate=r[0]
+                n=r[1]
+                frequency=r[2]
+                measure="tsr"#r[3]
+                #IMPLEMENTED ONLY FOR BIGRAMS !!!
+                '''
+                rcamps=candidate.split(" ")
+                if type=="strict":
+                    if rcamps[0] in firstcomponent and rcamps[-1] in lastcomponent:
+                        if not candidate in newcandidates:
+                            newcandidates[candidate]=frequency
+                            hashmeasure[candidate]=measure
+                            hashvalue[candidate]=value
+                            new=True
+                            firstcomponent[rcamps[0]]=1
+                            lastcomponent[rcamps[-1]]=1
+                elif type=="flexible":
+                    if rcamps[0] in firstcomponent or rcamps[-1] in lastcomponent:
+                        if not candidate in newcandidates:
+                            newcandidates[candidate]=frequency
+                            hashmeasure[candidate]=measure
+                            hashvalue[candidate]=value
+                            new=True
+                            firstcomponent[rcamps[0]]=1
+                            lastcomponent[rcamps[-1]]=1
+                            component[rcamps[0]]=1
+                            component[rcamps[-1]]=1
+                elif type=="combined":
+                    if iterations==1:
+                        if rcamps[0] in firstcomponent and rcamps[-1] in lastcomponent:
+                            if not candidate in newcandidates:
+                                newcandidates[candidate]=frequency
+                                hashmeasure[candidate]=measure
+                                hashvalue[candidate]=value
+                                new=True
+                                firstcomponent[rcamps[0]]=1
+                                lastcomponent[rcamps[-1]]=1
+                                component[rcamps[0]]=1
+                                component[rcamps[-1]]=1
+                    else:
+                        if rcamps[0] in firstcomponent or rcamps[-1] in lastcomponent:
+                            if not candidate in newcandidates:
+                                newcandidates[candidate]=frequency
+                                hashmeasure[candidate]=measure
+                                hashvalue[candidate]=value
+                                new=True
+                                firstcomponent[rcamps[0]]=1
+                                lastcomponent[rcamps[-1]]=1
+                                component[rcamps[0]]=1
+                                component[rcamps[-1]]=1
+                '''
+                first_c=False
+                middle_c=False
+                last_c=False
+                rcamps=candidate.split(" ")
+                truesfalses=[]
+                if str(rcamps[0]).lower() in firstcomponent: 
+                    first_c=True
+                    truesfalses.append(True)
+                else:
+                    truesfalses.append(False)
+                if str(rcamps[-1]).lower() in lastcomponent: 
+                    last_c=True
+                    truesfalses.append(True)
+                else:
+                    truesfalses.append(False)
+                if n>2:
+                    middle_c=True
+                    for i in range(1,n-1):
+                        if not str(r[i]).lower() in middlecomponent: middle_c=False
+                    if middle_c==True:
+                        truesfalses.append(True)
+                    else:
+                        truesfalses.append(False)
+                if type=="strict":
+                    if not False in truesfalses:
+                        if not candidate in newcandidates:
+                            newcandidates[candidate]=frequency
+                            hashmeasure[candidate]=measure
+                            hashvalue[candidate]=value
+                            new=True
+                            firstcomponent[rcamps[0]]=1
+                            lastcomponent[rcamps[-1]]=1
+                elif type=="flexible":
+                    if True in truesfalses:
+                        if not candidate in newcandidates:
+                            newcandidates[candidate]=frequency
+                            hashmeasure[candidate]=measure
+                            hashvalue[candidate]=value
+                            new=True
+                            firstcomponent[rcamps[0]]=1
+                            lastcomponent[rcamps[-1]]=1
+                            component[rcamps[0]]=1
+                            component[rcamps[-1]]=1
+                elif type=="combined":
+                    if iterations==1:       
+                        new=True
+                        if not False in truesfalses:
+                            if not candidate in newcandidates:
+                                newcandidates[candidate]=frequency
+                                hashmeasure[candidate]=measure
+                                hashvalue[candidate]=value                                
+                                firstcomponent[rcamps[0]]=1
+                                lastcomponent[rcamps[-1]]=1
+                                if n>2:
+                                    for i in range(1,n-1):
+                                        middlecomponent[rcamps[i]]=1
+                                        component[rcamps[i]]=1
+                    else:
+                        if True in truesfalses:
+                            if not candidate in newcandidates:
+                                newcandidates[candidate]=frequency
+                                hashmeasure[candidate]=measure
+                                hashvalue[candidate]=value
+                                new=True
+                                firstcomponent[rcamps[0]]=1
+                                lastcomponent[rcamps[-1]]=1
+                                if n>2:
+                                    for i in range(1,n-1):
+                                        middlecomponent[rcamps[i]]=1
+                                        component[rcamps[i]]=1
+                                component[rcamps[0]]=1
+                                component[rcamps[-1]]=1
+                '''
+                if n==2:
+                    if rcamps[0] in firstcomponent: first_c=True
+                    middle_c=True
+                    if rcamps[-1] in lastcomponent: last_c=True
+                    if type=="strict":
+                        if first_c and last_c:
+                            if not candidate in newcandidates:
+                                newcandidates[candidate]=frequency
+                                hashmeasure[candidate]=measure
+                                hashvalue[candidate]=value
+                                new=True
+                                firstcomponent[rcamps[0]]=1
+                                lastcomponent[rcamps[-1]]=1
+                    elif type=="flexible":
+                        if first_c or last_c:
+                            if not candidate in newcandidates:
+                                newcandidates[candidate]=frequency
+                                hashmeasure[candidate]=measure
+                                hashvalue[candidate]=value
+                                new=True
+                                firstcomponent[rcamps[0]]=1
+                                lastcomponent[rcamps[-1]]=1
+                                component[rcamps[0]]=1
+                                component[rcamps[-1]]=1
+                    
+                    elif type=="combined":
+                        if iterations==1:
+                            if first_c and last_c:
+                                if not candidate in newcandidates:
+                                    newcandidates[candidate]=frequency
+                                    hashmeasure[candidate]=measure
+                                    hashvalue[candidate]=value
+                                    new=True
+                                    firstcomponent[rcamps[0]]=1
+                                    lastcomponent[rcamps[-1]]=1
+                                    component[rcamps[0]]=1
+                                    component[rcamps[-1]]=1
+                        else:
+                            if first_c or last_c:
+                                if not candidate in newcandidates:
+                                    newcandidates[candidate]=frequency
+                                    hashmeasure[candidate]=measure
+                                    hashvalue[candidate]=value
+                                    new=True
+                                    firstcomponent[rcamps[0]]=1
+                                    lastcomponent[rcamps[-1]]=1
+                                    component[rcamps[0]]=1
+                                    component[rcamps[-1]]=1
+                                    
+                elif n==3:
+                    if rcamps[0] in firstcomponent: first_c=True
+                    if rcamps[1] in middlecomponent: middle_c=True
+                    if rcamps[-1] in lastcomponent: last_c=True
+                    if type=="strict":
+                        if first_c and middle_c and last_c:
+                            if not candidate in newcandidates:
+                                newcandidates[candidate]=frequency
+                                hashmeasure[candidate]=measure
+                                hashvalue[candidate]=value
+                                new=True
+                                firstcomponent[rcamps[0]]=1
+                                middlecomponent[rcamps[1]]=1
+                                lastcomponent[rcamps[-1]]=1
+                    elif type=="flexible":
+                        condition=False
+                        if first_c and middle_c or last_c: condition=True
+                        #if first_c or middle_c and last_c: condition=True
+                        if last_c and middle_c or first_c: condition=True
+                        if condition:
+                            if not candidate in newcandidates:
+                                newcandidates[candidate]=frequency
+                                hashmeasure[candidate]=measure
+                                hashvalue[candidate]=value
+                                new=True
+                                firstcomponent[rcamps[0]]=1
+                                middlecomponent[rcamps[1]]=1
+                                lastcomponent[rcamps[-1]]=1
+                                component[rcamps[0]]=1
+                                component[rcamps[1]]=1
+                                component[rcamps[-1]]=1
+                    
+                    elif type=="combined":
+                        if iterations==1:
+                            if first_c and middle_c and last_c:
+                                if not candidate in newcandidates:
+                                    newcandidates[candidate]=frequency
+                                    hashmeasure[candidate]=measure
+                                    hashvalue[candidate]=value
+                                    new=True
+                                    firstcomponent[rcamps[0]]=1
+                                    middlecomponent[rcamps[1]]=1
+                                    lastcomponent[rcamps[-1]]=1
+                                    component[rcamps[0]]=1
+                                    component[rcamps[1]]=1
+                                    component[rcamps[-1]]=1
+                        else:
+                            condition=False
+                            if first_c or middle_c or last_c: condition=True
+                            #if first_c and middle_c or last_c: condition=True
+                            #if first_c or middle_c and last_c: condition=True
+                            #if last_c and middle_c or first_c: condition=True
+                            if condition:
+                                if not candidate in newcandidates:
+                                    newcandidates[candidate]=frequency
+                                    hashmeasure[candidate]=measure
+                                    hashvalue[candidate]=value
+                                    new=True
+                                    firstcomponent[rcamps[0]]=1
+                                    middlecomponent[rcamps[1]]=1
+                                    lastcomponent[rcamps[-1]]=1
+                                    component[rcamps[0]]=1
+                                    component[rcamps[1]]=1
+                                    component[rcamps[-1]]=1
+            '''     
+            if iterations>=max_iterations:
+                break
+            if verbose: print(iterations,new)
+        with self.conn:
+            self.cur.execute('DELETE FROM term_candidates')
+            self.conn.commit()
+        
+                    
+        data=[]
+        for c in newcandidates:
+            termb=c
+            n=len(c.split(" "))
+            freqtotal=newcandidates[c]
+            measure=hashmeasure[c]
+            value=hashvalue[c]
+            record=[]
+            record.append(termb)
+            record.append(n)
+            record.append(freqtotal)
+            record.append(measure)
+            record.append(value)
+            data.append(record)
+        with self.conn:
+            self.cur.executemany("INSERT INTO term_candidates (candidate, n, frequency, measure, value) VALUES (?,?,?,?,?)",data)
+            
+        self.conn.commit()   
+
+def L_LLR(a,b,c):
+    '''Auxiliar function to calculate Log Likelihood Ratio'''
+    L=(c**a)*((1-c)**(b-a))
+    return(L)
+            
+class myBigramAssocMeasures(nltk.collocations.BigramAssocMeasures):
+    """
+    A collection of bigram association measures. Each association measure
+    is provided as a function with three arguments::
+
+        bigram_score_fn(n_ii, (n_ix, n_xi), n_xx)
+
+    The arguments constitute the marginals of a contingency table, counting
+    the occurrences of particular events in a corpus. The letter i in the
+    suffix refers to the appearance of the word in question, while x indicates
+    the appearance of any word. Thus, for example:
+
+        n_ii counts (w1, w2), i.e. the bigram being scored
+        n_ix counts (w1, *)
+        n_xi counts (*, w2)
+        n_xx counts (*, *), i.e. any bigram
+
+    This may be shown with respect to a contingency table::
+
+                w1    ~w1
+             ------ ------
+         w2 | n_ii | n_oi | = n_xi
+             ------ ------
+        ~w2 | n_io | n_oo |
+             ------ ------
+             = n_ix        TOTAL = n_xx
+             
+    Amb la terminologia de Pazienza
+
+                w1    ~w1
+             ------ ------
+         w2 | n_ii O11| n_oi O12| = n_xi
+             ------ ------
+        ~w2 | n_io O21| n_oo O22|
+             ------ ------
+             = n_ix        TOTAL = n_xx
+             
+             N=O11+O12+O21+O22= n_xx
+             R1=O11+O12 = n_xi
+             R2=O21+O22
+             C1=O11+O21=n_ix
+             C2=O12+O22= n_oi+n_oo
+             
+        TENIM: n_ii, n_ix_xi_tuple, n_xx
+        n_io=n_ix-n_ii
+        n_oi=n_xi-n_ii
+        n_oo=n_xx-n_ix-n_xi
+    """
+    #MEASURES NOT IMPLEMENTED IN NLTK
+    
+    def loglikelihood(self,n_ii, n_ix_xi_tuple, n_xx):
+        '''LogLikelihood according to NSP'''
+        (n_ix, n_xi) = n_ix_xi_tuple
+        n_oo=n_xx-n_ix-n_xi
+        n_io=n_ix-n_ii
+        n_oi=n_xi-n_ii
+        n_oo=n_xx-n_ix-n_xi        
+        
+        n11=n_ii
+        n12=n_io
+        n21=n_oi
+        n22=n_oo
+        n1p=n11+n12
+        np1=n11+n21
+        n2p=n21+n22
+        np2=n12+n22
+        npp=n_xx
+
+        m11 = (n1p*np1/npp)
+        m12 = (n1p*np2/npp)
+        m21 = (np1*n2p/npp)
+        m22 = (n2p*np2/npp)
+        try:
+            LogLikelihood = 2 * (n11 * math.log((n11/m11),2) + n12 * math.log((n12/m12),2) + n21 * math.log((n21/m21),2) + n22 * math.log((n22/m22),2))
+        except:
+            LogLikelihood=0
+        return(LogLikelihood)
+        
+    def MI(self,n_ii, n_ix_xi_tuple, n_xx):
+        '''Church Mutual Information accoding to Pazienza'''
+        (n_ix, n_xi) = n_ix_xi_tuple
+        self.E11=n_xi*n_ix/n_xx
+        self.part=n_ii/self.E11
+        self.MI=math.log(self.part,2)
+        return(self.MI)
+        
+    def MI2(self,n_ii, n_ix_xi_tuple, n_xx):
+        '''Church Mutual Information Variant accoding to Pazienza'''
+        (n_ix, n_xi) = n_ix_xi_tuple
+        self.E11=n_xi*n_ix/n_xx
+        self.part=(n_ii/self.E11)**2
+        self.MI2=math.log(self.part,2)
+        return(self.MI2)
+        
+    def MI3(self,n_ii, n_ix_xi_tuple, n_xx):
+        '''Church Mutual Information Variant accoding to Pazienza'''
+        (n_ix, n_xi) = n_ix_xi_tuple
+        self.E11=n_xi*n_ix/n_xx
+        self.part=(n_ii/self.E11)**3
+        self.MI3=math.log(self.part,2)
+        return(self.MI3)
+        
+    def odds(self,n_ii, n_ix_xi_tuple, n_xx):
+        '''Odds ratio according to NSP'''
+        (n_ix, n_xi) = n_ix_xi_tuple
+        n_oo=n_xx-n_ix-n_xi
+        n_io=n_ix-n_ii
+        n_oi=n_xi-n_ii
+        n_oo=n_xx-n_ix-n_xi        
+        
+        n11=n_ii
+        n12=n_io
+        n21=n_oi
+        n22=n_oo
+        n1p=n11+n12
+        np1=n11+n21
+        n2p=n21+n22
+        np2=n12+n22
+        npp=n_xx
+
+        m11 = (n1p*np1/npp)
+        m12 = (n1p*np2/npp)
+        m21 = (np1*n2p/npp)
+        m22 = (n2p*np2/npp)
+        
+        if n21==0:n21=1
+        if n12==0:n12=1
+        ODDS_RATIO = (n11*n22)/(n21*n12)
+        return(ODDS_RATIO)
+        
+    def z_score(self,n_ii, n_ix_xi_tuple, n_xx):
+        '''z-score ratio according to NSP'''
+        (n_ix, n_xi) = n_ix_xi_tuple
+        n_oo=n_xx-n_ix-n_xi
+        n_io=n_ix-n_ii
+        n_oi=n_xi-n_ii
+        n_oo=n_xx-n_ix-n_xi        
+        
+        n11=n_ii
+        n12=n_io
+        n21=n_oi
+        n22=n_oo
+        n1p=n11+n12
+        np1=n11+n21
+        n2p=n21+n22
+        np2=n12+n22
+        npp=n_xx
+
+        m11 = (n1p*np1/npp)
+        m12 = (n1p*np2/npp)
+        m21 = (np1*n2p/npp)
+        m22 = (n2p*np2/npp)
+       
+        zscore = (n11-m11)/(math.sqrt(m11))
+        return(zscore)
+   
+
+class myTrigramAssocMeasures(nltk.collocations.TrigramAssocMeasures):
+    pass
+    
+class myQuadgramAssocMeasures(nltk.collocations.QuadgramAssocMeasures):
+    pass
+
+ 
+  
+    
diff --git a/embeddings.py b/embeddings.py
new file mode 100644
index 0000000..9a407a5
--- /dev/null
+++ b/embeddings.py
@@ -0,0 +1,80 @@
+# Copyright (C) 2016-2018  Mikel Artetxe <artetxem@gmail.com>
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+from cupy_utils import *
+
+import numpy as np
+
+
+def read(file, threshold=0, vocabulary=None, dtype='float'):
+    header = file.readline().split(' ')
+    count = int(header[0]) if threshold <= 0 else min(threshold, int(header[0]))
+    dim = int(header[1])
+    words = []
+    matrix = np.empty((count, dim), dtype=dtype) if vocabulary is None else []
+    for i in range(count):
+        word, vec = file.readline().split(' ', 1)
+        if vocabulary is None:
+            words.append(word)
+            matrix[i] = np.fromstring(vec, sep=' ', dtype=dtype)
+        elif word in vocabulary:
+            words.append(word)
+            matrix.append(np.fromstring(vec, sep=' ', dtype=dtype))
+    return (words, matrix) if vocabulary is None else (words, np.array(matrix, dtype=dtype))
+
+
+def write(words, matrix, file):
+    m = asnumpy(matrix)
+    print('%d %d' % m.shape, file=file)
+    for i in range(len(words)):
+        print(words[i] + ' ' + ' '.join(['%.6g' % x for x in m[i]]), file=file)
+
+
+def length_normalize(matrix):
+    xp = get_array_module(matrix)
+    norms = xp.sqrt(xp.sum(matrix**2, axis=1))
+    norms[norms == 0] = 1
+    matrix /= norms[:, xp.newaxis]
+
+
+def mean_center(matrix):
+    xp = get_array_module(matrix)
+    avg = xp.mean(matrix, axis=0)
+    matrix -= avg
+
+
+def length_normalize_dimensionwise(matrix):
+    xp = get_array_module(matrix)
+    norms = xp.sqrt(xp.sum(matrix**2, axis=0))
+    norms[norms == 0] = 1
+    matrix /= norms
+
+
+def mean_center_embeddingwise(matrix):
+    xp = get_array_module(matrix)
+    avg = xp.mean(matrix, axis=1)
+    matrix -= avg[:, xp.newaxis]
+
+
+def normalize(matrix, actions):
+    for action in actions:
+        if action == 'unit':
+            length_normalize(matrix)
+        elif action == 'center':
+            mean_center(matrix)
+        elif action == 'unitdim':
+            length_normalize_dimensionwise(matrix)
+        elif action == 'centeremb':
+            mean_center_embeddingwise(matrix)
diff --git a/map_embeddings.py b/map_embeddings.py
new file mode 100644
index 0000000..039749e
--- /dev/null
+++ b/map_embeddings.py
@@ -0,0 +1,696 @@
+# Copyright (C) 2016-2018  Mikel Artetxe <artetxem@gmail.com>
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+import embeddings
+from cupy_utils import *
+
+import argparse
+import collections
+import numpy as np
+import re
+import sys
+import time
+
+
+def dropout(m, p):
+    if p <= 0.0:
+        return m
+    else:
+        xp = get_array_module(m)
+        mask = xp.random.rand(*m.shape) >= p
+        return m*mask
+
+
+def topk_mean(m, k, inplace=False):  # TODO Assuming that axis is 1
+    xp = get_array_module(m)
+    n = m.shape[0]
+    ans = xp.zeros(n, dtype=m.dtype)
+    if k <= 0:
+        return ans
+    if not inplace:
+        m = xp.array(m)
+    ind0 = xp.arange(n)
+    ind1 = xp.empty(n, dtype=int)
+    minimum = m.min()
+    for i in range(k):
+        m.argmax(axis=1, out=ind1)
+        ans += m[ind0, ind1]
+        m[ind0, ind1] = minimum
+    return ans / k
+
+def supervised_mapping(src_input,trg_input,src_output,trg_output,init_dictionary,encoding="utf-8",precision="fp32",cuda=False,batch_size=1000,seed=0,unsupervised_vocab=0,src_reweight=0,trg_reweight=0,dim_reduction=0,vocabulary_cutoff=0,direction="union",csls=0,threshold=0.000001,validation=None,stochastic_initial=0.1,stochastic_multiplier=2.0,stochastic_interval=50):    
+    self_learning=False
+    print("SUPERVISED")
+    normalize=['unit', 'center', 'unit']
+    #parser.set_defaults(init_dictionary=args.supervised, normalize=['unit', 'center', 'unit'], whiten=True, src_reweight=0.5, trg_reweight=0.5, src_dewhiten='src', trg_dewhiten='trg', batch_size=1000)
+    normalize=['unit', 'center', 'unit']
+    whiten=True
+    src_reweight=0.5
+    trg_reweight=0.5
+    src_dewhiten='src'
+    trg_dewhiten='trg'
+    batch_size=1000
+    cuda=False
+    identical=False
+    unsupervised=False
+    init_identical=False
+    init_numerals=False
+    init_unsupervised=False
+    orthogonal=False
+    unconstrained=False
+    self_learning=False
+    verbose=False
+    if precision == 'fp16':
+        dtype = 'float16'
+    elif precision == 'fp32':
+        dtype = 'float32'
+    elif precision == 'fp64':
+        dtype = 'float64'
+
+    # Read input embeddings
+    print("Read input embeddings")
+    srcfile = open(src_input, encoding=encoding, errors='surrogateescape')
+    trgfile = open(trg_input, encoding=encoding, errors='surrogateescape')
+    src_words, x = embeddings.read(srcfile, dtype=dtype)
+    trg_words, z = embeddings.read(trgfile, dtype=dtype)
+
+    # NumPy/CuPy management
+    if cuda:
+        if not supports_cupy():
+            print('ERROR: Install CuPy for CUDA support', file=sys.stderr)
+            sys.exit(-1)
+        xp = get_cupy()
+        x = xp.asarray(x)
+        z = xp.asarray(z)
+    else:
+        xp = np
+    xp.random.seed(seed)
+
+    # Build word to index map
+    print("Build word to index map")
+    src_word2ind = {word: i for i, word in enumerate(src_words)}
+    trg_word2ind = {word: i for i, word in enumerate(trg_words)}
+
+    # STEP 0: Normalization
+    print("STEP 0: Normalization")
+    embeddings.normalize(x, normalize)
+    embeddings.normalize(z, normalize)
+
+    # Build the seed dictionary
+    print("Build the seed dictionary")
+    src_indices = []
+    trg_indices = []
+    
+    f = open(init_dictionary, encoding=encoding, errors='surrogateescape')
+    for line in f:
+        src, trg = line.split()
+        try:
+            src_ind = src_word2ind[src]
+            trg_ind = trg_word2ind[trg]
+            src_indices.append(src_ind)
+            trg_indices.append(trg_ind)
+        except KeyError:
+            print('WARNING: OOV dictionary entry ({0} - {1})'.format(src, trg), file=sys.stderr)
+
+    # Read validation dictionary
+    if validation is not None:
+        f = open(validation, encoding=encoding, errors='surrogateescape')
+        validation = collections.defaultdict(set)
+        oov = set()
+        vocab = set()
+        for line in f:
+            src, trg = line.split()
+            try:
+                src_ind = src_word2ind[src]
+                trg_ind = trg_word2ind[trg]
+                validation[src_ind].add(trg_ind)
+                vocab.add(src)
+            except KeyError:
+                oov.add(src)
+        oov -= vocab  # If one of the translation options is in the vocabulary, then the entry is not an oov
+        validation_coverage = len(validation) / (len(validation) + len(oov))
+
+    
+
+    # Allocate memory
+    print("Allocate memory")
+    xw = xp.empty_like(x)
+    zw = xp.empty_like(z)
+    src_size = x.shape[0] if vocabulary_cutoff <= 0 else min(x.shape[0], vocabulary_cutoff)
+    trg_size = z.shape[0] if vocabulary_cutoff <= 0 else min(z.shape[0], vocabulary_cutoff)
+    simfwd = xp.empty((batch_size, trg_size), dtype=dtype)
+    simbwd = xp.empty((batch_size, src_size), dtype=dtype)
+    if validation is not None:
+        simval = xp.empty((len(validation.keys()), z.shape[0]), dtype=dtype)
+
+    best_sim_forward = xp.full(src_size, -100, dtype=dtype)
+    src_indices_forward = xp.arange(src_size)
+    trg_indices_forward = xp.zeros(src_size, dtype=int)
+    best_sim_backward = xp.full(trg_size, -100, dtype=dtype)
+    src_indices_backward = xp.zeros(trg_size, dtype=int)
+    trg_indices_backward = xp.arange(trg_size)
+    knn_sim_fwd = xp.zeros(src_size, dtype=dtype)
+    knn_sim_bwd = xp.zeros(trg_size, dtype=dtype)
+
+    # Training loop
+    print("Training loop")
+    best_objective = objective = -100.
+    it = 1
+    last_improvement = 0
+    keep_prob = stochastic_initial
+    t = time.time()
+    end = not self_learning
+    while True:
+
+        # Increase the keep probability if we have not improve in stochastic_interval iterations
+        if it - last_improvement > stochastic_interval:
+            if keep_prob >= 1.0:
+                end = True
+            keep_prob = min(1.0, stochastic_multiplier*keep_prob)
+            last_improvement = it
+
+        # Update the embedding mapping
+        if orthogonal or not end:  # orthogonal mapping
+            u, s, vt = xp.linalg.svd(z[trg_indices].T.dot(x[src_indices]))
+            w = vt.T.dot(u.T)
+            x.dot(w, out=xw)
+            zw[:] = z
+        elif unconstrained:  # unconstrained mapping
+            x_pseudoinv = xp.linalg.inv(x[src_indices].T.dot(x[src_indices])).dot(x[src_indices].T)
+            w = x_pseudoinv.dot(z[trg_indices])
+            x.dot(w, out=xw)
+            zw[:] = z
+        else:  # advanced mapping
+
+            # TODO xw.dot(wx2, out=xw) and alike not working
+            xw[:] = x
+            zw[:] = z
+
+            # STEP 1: Whitening
+            def whitening_transformation(m):
+                u, s, vt = xp.linalg.svd(m, full_matrices=False)
+                return vt.T.dot(xp.diag(1/s)).dot(vt)
+            if whiten:
+                wx1 = whitening_transformation(xw[src_indices])
+                wz1 = whitening_transformation(zw[trg_indices])
+                xw = xw.dot(wx1)
+                zw = zw.dot(wz1)
+
+            # STEP 2: Orthogonal mapping
+            wx2, s, wz2_t = xp.linalg.svd(xw[src_indices].T.dot(zw[trg_indices]))
+            wz2 = wz2_t.T
+            xw = xw.dot(wx2)
+            zw = zw.dot(wz2)
+
+            # STEP 3: Re-weighting
+            xw *= s**src_reweight
+            zw *= s**trg_reweight
+
+            # STEP 4: De-whitening
+            if src_dewhiten == 'src':
+                xw = xw.dot(wx2.T.dot(xp.linalg.inv(wx1)).dot(wx2))
+            elif src_dewhiten == 'trg':
+                xw = xw.dot(wz2.T.dot(xp.linalg.inv(wz1)).dot(wz2))
+            if trg_dewhiten == 'src':
+                zw = zw.dot(wx2.T.dot(xp.linalg.inv(wx1)).dot(wx2))
+            elif trg_dewhiten == 'trg':
+                zw = zw.dot(wz2.T.dot(xp.linalg.inv(wz1)).dot(wz2))
+
+            # STEP 5: Dimensionality reduction
+            if dim_reduction > 0:
+                xw = xw[:, :dim_reduction]
+                zw = zw[:, :dim_reduction]
+
+        # Self-learning
+        if end:
+            break
+        else:
+            # Update the training dictionary
+            if direction in ('forward', 'union'):
+                if csls_neighborhood > 0:
+                    for i in range(0, trg_size, simbwd.shape[0]):
+                        j = min(i + simbwd.shape[0], trg_size)
+                        zw[i:j].dot(xw[:src_size].T, out=simbwd[:j-i])
+                        knn_sim_bwd[i:j] = topk_mean(simbwd[:j-i], k=csls_neighborhood, inplace=True)
+                for i in range(0, src_size, simfwd.shape[0]):
+                    j = min(i + simfwd.shape[0], src_size)
+                    xw[i:j].dot(zw[:trg_size].T, out=simfwd[:j-i])
+                    simfwd[:j-i].max(axis=1, out=best_sim_forward[i:j])
+                    simfwd[:j-i] -= knn_sim_bwd/2  # Equivalent to the real CSLS scores for NN
+                    dropout(simfwd[:j-i], 1 - keep_prob).argmax(axis=1, out=trg_indices_forward[i:j])
+            if direction in ('backward', 'union'):
+                if csls_neighborhood > 0:
+                    for i in range(0, src_size, simfwd.shape[0]):
+                        j = min(i + simfwd.shape[0], src_size)
+                        xw[i:j].dot(zw[:trg_size].T, out=simfwd[:j-i])
+                        knn_sim_fwd[i:j] = topk_mean(simfwd[:j-i], k=csls_neighborhood, inplace=True)
+                for i in range(0, trg_size, simbwd.shape[0]):
+                    j = min(i + simbwd.shape[0], trg_size)
+                    zw[i:j].dot(xw[:src_size].T, out=simbwd[:j-i])
+                    simbwd[:j-i].max(axis=1, out=best_sim_backward[i:j])
+                    simbwd[:j-i] -= knn_sim_fwd/2  # Equivalent to the real CSLS scores for NN
+                    dropout(simbwd[:j-i], 1 - keep_prob).argmax(axis=1, out=src_indices_backward[i:j])
+            if direction == 'forward':
+                src_indices = src_indices_forward
+                trg_indices = trg_indices_forward
+            elif direction == 'backward':
+                src_indices = src_indices_backward
+                trg_indices = trg_indices_backward
+            elif direction == 'union':
+                src_indices = xp.concatenate((src_indices_forward, src_indices_backward))
+                trg_indices = xp.concatenate((trg_indices_forward, trg_indices_backward))
+
+            # Objective function evaluation
+            if direction == 'forward':
+                objective = xp.mean(best_sim_forward).tolist()
+            elif direction == 'backward':
+                objective = xp.mean(best_sim_backward).tolist()
+            elif direction == 'union':
+                objective = (xp.mean(best_sim_forward) + xp.mean(best_sim_backward)).tolist() / 2
+            if objective - best_objective >= threshold:
+                last_improvement = it
+                best_objective = objective
+
+            # Accuracy and similarity evaluation in validation
+            if validation is not None:
+                src = list(validation.keys())
+                xw[src].dot(zw.T, out=simval)
+                nn = asnumpy(simval.argmax(axis=1))
+                accuracy = np.mean([1 if nn[i] in validation[src[i]] else 0 for i in range(len(src))])
+                similarity = np.mean([max([simval[i, j].tolist() for j in validation[src[i]]]) for i in range(len(src))])
+
+            # Logging
+            duration = time.time() - t
+            if verbose:
+                print(file=sys.stderr)
+                print('ITERATION {0} ({1:.2f}s)'.format(it, duration), file=sys.stderr)
+                print('\t- Objective:        {0:9.4f}%'.format(100 * objective), file=sys.stderr)
+                print('\t- Drop probability: {0:9.4f}%'.format(100 - 100*keep_prob), file=sys.stderr)
+                if validation is not None:
+                    print('\t- Val. similarity:  {0:9.4f}%'.format(100 * similarity), file=sys.stderr)
+                    print('\t- Val. accuracy:    {0:9.4f}%'.format(100 * accuracy), file=sys.stderr)
+                    print('\t- Val. coverage:    {0:9.4f}%'.format(100 * validation_coverage), file=sys.stderr)
+                sys.stderr.flush()
+            if log is not None:
+                val = '{0:.6f}\t{1:.6f}\t{2:.6f}'.format(
+                    100 * similarity, 100 * accuracy, 100 * validation_coverage) if validation is not None else ''
+                print('{0}\t{1:.6f}\t{2}\t{3:.6f}'.format(it, 100 * objective, val, duration), file=log)
+                log.flush()
+
+        t = time.time()
+        it += 1
+
+    # Write mapped embeddings
+    print("Write mapped embeddings")
+    srcfile = open(src_output, mode='w', encoding=encoding, errors='surrogateescape')
+    trgfile = open(trg_output, mode='w', encoding=encoding, errors='surrogateescape')
+    embeddings.write(src_words, xw, srcfile)
+    embeddings.write(trg_words, zw, trgfile)
+    srcfile.close()
+    trgfile.close()    
+    
+    
+def main():
+    # Parse command line arguments
+    parser = argparse.ArgumentParser(description='Map word embeddings in two languages into a shared space')
+    parser.add_argument('src_input', help='the input source embeddings')
+    parser.add_argument('trg_input', help='the input target embeddings')
+    parser.add_argument('src_output', help='the output source embeddings')
+    parser.add_argument('trg_output', help='the output target embeddings')
+    parser.add_argument('--encoding', default='utf-8', help='the character encoding for input/output (defaults to utf-8)')
+    parser.add_argument('--precision', choices=['fp16', 'fp32', 'fp64'], default='fp32', help='the floating-point precision (defaults to fp32)')
+    parser.add_argument('--cuda', action='store_true', help='use cuda (requires cupy)')
+    parser.add_argument('--batch_size', default=10000, type=int, help='batch size (defaults to 10000); does not affect results, larger is usually faster but uses more memory')
+    parser.add_argument('--seed', type=int, default=0, help='the random seed (defaults to 0)')
+
+    recommended_group = parser.add_argument_group('recommended settings', 'Recommended settings for different scenarios')
+    recommended_type = recommended_group.add_mutually_exclusive_group()
+    recommended_type.add_argument('--supervised', metavar='DICTIONARY', help='recommended if you have a large training dictionary')
+    recommended_type.add_argument('--semi_supervised', metavar='DICTIONARY', help='recommended if you have a small seed dictionary')
+    recommended_type.add_argument('--identical', action='store_true', help='recommended if you have no seed dictionary but can rely on identical words')
+    recommended_type.add_argument('--unsupervised', action='store_true', help='recommended if you have no seed dictionary and do not want to rely on identical words')
+    recommended_type.add_argument('--acl2018', action='store_true', help='reproduce our ACL 2018 system')
+    recommended_type.add_argument('--aaai2018', metavar='DICTIONARY', help='reproduce our AAAI 2018 system')
+    recommended_type.add_argument('--acl2017', action='store_true', help='reproduce our ACL 2017 system with numeral initialization')
+    recommended_type.add_argument('--acl2017_seed', metavar='DICTIONARY', help='reproduce our ACL 2017 system with a seed dictionary')
+    recommended_type.add_argument('--emnlp2016', metavar='DICTIONARY', help='reproduce our EMNLP 2016 system')
+
+    init_group = parser.add_argument_group('advanced initialization arguments', 'Advanced initialization arguments')
+    init_type = init_group.add_mutually_exclusive_group()
+    init_type.add_argument('-d', '--init_dictionary', default=sys.stdin.fileno(), metavar='DICTIONARY', help='the training dictionary file (defaults to stdin)')
+    init_type.add_argument('--init_identical', action='store_true', help='use identical words as the seed dictionary')
+    init_type.add_argument('--init_numerals', action='store_true', help='use latin numerals (i.e. words matching [0-9]+) as the seed dictionary')
+    init_type.add_argument('--init_unsupervised', action='store_true', help='use unsupervised initialization')
+    init_group.add_argument('--unsupervised_vocab', type=int, default=0, help='restrict the vocabulary to the top k entries for unsupervised initialization')
+
+    mapping_group = parser.add_argument_group('advanced mapping arguments', 'Advanced embedding mapping arguments')
+    mapping_group.add_argument('--normalize', choices=['unit', 'center', 'unitdim', 'centeremb', 'none'], nargs='*', default=[], help='the normalization actions to perform in order')
+    mapping_group.add_argument('--whiten', action='store_true', help='whiten the embeddings')
+    mapping_group.add_argument('--src_reweight', type=float, default=0, nargs='?', const=1, help='re-weight the source language embeddings')
+    mapping_group.add_argument('--trg_reweight', type=float, default=0, nargs='?', const=1, help='re-weight the target language embeddings')
+    mapping_group.add_argument('--src_dewhiten', choices=['src', 'trg'], help='de-whiten the source language embeddings')
+    mapping_group.add_argument('--trg_dewhiten', choices=['src', 'trg'], help='de-whiten the target language embeddings')
+    mapping_group.add_argument('--dim_reduction', type=int, default=0, help='apply dimensionality reduction')
+    mapping_type = mapping_group.add_mutually_exclusive_group()
+    mapping_type.add_argument('-c', '--orthogonal', action='store_true', help='use orthogonal constrained mapping')
+    mapping_type.add_argument('-u', '--unconstrained', action='store_true', help='use unconstrained mapping')
+
+    self_learning_group = parser.add_argument_group('advanced self-learning arguments', 'Advanced arguments for self-learning')
+    self_learning_group.add_argument('--self_learning', action='store_true', help='enable self-learning')
+    self_learning_group.add_argument('--vocabulary_cutoff', type=int, default=0, help='restrict the vocabulary to the top k entries')
+    self_learning_group.add_argument('--direction', choices=['forward', 'backward', 'union'], default='union', help='the direction for dictionary induction (defaults to union)')
+    self_learning_group.add_argument('--csls', type=int, nargs='?', default=0, const=10, metavar='NEIGHBORHOOD_SIZE', dest='csls_neighborhood', help='use CSLS for dictionary induction')
+    self_learning_group.add_argument('--threshold', default=0.000001, type=float, help='the convergence threshold (defaults to 0.000001)')
+    self_learning_group.add_argument('--validation', default=None, metavar='DICTIONARY', help='a dictionary file for validation at each iteration')
+    self_learning_group.add_argument('--stochastic_initial', default=0.1, type=float, help='initial keep probability stochastic dictionary induction (defaults to 0.1)')
+    self_learning_group.add_argument('--stochastic_multiplier', default=2.0, type=float, help='stochastic dictionary induction multiplier (defaults to 2.0)')
+    self_learning_group.add_argument('--stochastic_interval', default=50, type=int, help='stochastic dictionary induction interval (defaults to 50)')
+    self_learning_group.add_argument('--log', help='write to a log file in tsv format at each iteration')
+    self_learning_group.add_argument('-v', '--verbose', action='store_true', help='write log information to stderr at each iteration')
+    args = parser.parse_args()
+
+    if args.supervised is not None:
+        parser.set_defaults(init_dictionary=args.supervised, normalize=['unit', 'center', 'unit'], whiten=True, src_reweight=0.5, trg_reweight=0.5, src_dewhiten='src', trg_dewhiten='trg', batch_size=1000)
+    if args.semi_supervised is not None:
+        parser.set_defaults(init_dictionary=args.semi_supervised, normalize=['unit', 'center', 'unit'], whiten=True, src_reweight=0.5, trg_reweight=0.5, src_dewhiten='src', trg_dewhiten='trg', self_learning=True, vocabulary_cutoff=20000, csls_neighborhood=10)
+    if args.identical:
+        parser.set_defaults(init_identical=True, normalize=['unit', 'center', 'unit'], whiten=True, src_reweight=0.5, trg_reweight=0.5, src_dewhiten='src', trg_dewhiten='trg', self_learning=True, vocabulary_cutoff=20000, csls_neighborhood=10)
+    if args.unsupervised or args.acl2018:
+        parser.set_defaults(init_unsupervised=True, unsupervised_vocab=4000, normalize=['unit', 'center', 'unit'], whiten=True, src_reweight=0.5, trg_reweight=0.5, src_dewhiten='src', trg_dewhiten='trg', self_learning=True, vocabulary_cutoff=20000, csls_neighborhood=10)
+    if args.aaai2018:
+        parser.set_defaults(init_dictionary=args.aaai2018, normalize=['unit', 'center'], whiten=True, trg_reweight=1, src_dewhiten='src', trg_dewhiten='trg', batch_size=1000)
+    if args.acl2017:
+        parser.set_defaults(init_numerals=True, orthogonal=True, normalize=['unit', 'center'], self_learning=True, direction='forward', stochastic_initial=1.0, stochastic_interval=1, batch_size=1000)
+    if args.acl2017_seed:
+        parser.set_defaults(init_dictionary=args.acl2017_seed, orthogonal=True, normalize=['unit', 'center'], self_learning=True, direction='forward', stochastic_initial=1.0, stochastic_interval=1, batch_size=1000)
+    if args.emnlp2016:
+        parser.set_defaults(init_dictionary=args.emnlp2016, orthogonal=True, normalize=['unit', 'center'], batch_size=1000)
+    args = parser.parse_args()
+
+    # Check command line arguments
+    if (args.src_dewhiten is not None or args.trg_dewhiten is not None) and not args.whiten:
+        print('ERROR: De-whitening requires whitening first', file=sys.stderr)
+        sys.exit(-1)
+
+    # Choose the right dtype for the desired precision
+    if args.precision == 'fp16':
+        dtype = 'float16'
+    elif args.precision == 'fp32':
+        dtype = 'float32'
+    elif args.precision == 'fp64':
+        dtype = 'float64'
+
+    # Read input embeddings
+    
+    srcfile = open(args.src_input, encoding=args.encoding, errors='surrogateescape')
+    trgfile = open(args.trg_input, encoding=args.encoding, errors='surrogateescape')
+    src_words, x = embeddings.read(srcfile, dtype=dtype)
+    trg_words, z = embeddings.read(trgfile, dtype=dtype)
+
+    # NumPy/CuPy management
+    if args.cuda:
+        if not supports_cupy():
+            print('ERROR: Install CuPy for CUDA support', file=sys.stderr)
+            sys.exit(-1)
+        xp = get_cupy()
+        x = xp.asarray(x)
+        z = xp.asarray(z)
+    else:
+        xp = np
+    xp.random.seed(args.seed)
+
+    # Build word to index map
+    
+    src_word2ind = {word: i for i, word in enumerate(src_words)}
+    trg_word2ind = {word: i for i, word in enumerate(trg_words)}
+
+    # STEP 0: Normalization
+    
+    embeddings.normalize(x, args.normalize)
+    embeddings.normalize(z, args.normalize)
+
+    # Build the seed dictionary
+    src_indices = []
+    trg_indices = []
+    if args.init_unsupervised:
+        sim_size = min(x.shape[0], z.shape[0]) if args.unsupervised_vocab <= 0 else min(x.shape[0], z.shape[0], args.unsupervised_vocab)
+        u, s, vt = xp.linalg.svd(x[:sim_size], full_matrices=False)
+        xsim = (u*s).dot(u.T)
+        u, s, vt = xp.linalg.svd(z[:sim_size], full_matrices=False)
+        zsim = (u*s).dot(u.T)
+        del u, s, vt
+        xsim.sort(axis=1)
+        zsim.sort(axis=1)
+        embeddings.normalize(xsim, args.normalize)
+        embeddings.normalize(zsim, args.normalize)
+        sim = xsim.dot(zsim.T)
+        if args.csls_neighborhood > 0:
+            knn_sim_fwd = topk_mean(sim, k=args.csls_neighborhood)
+            knn_sim_bwd = topk_mean(sim.T, k=args.csls_neighborhood)
+            sim -= knn_sim_fwd[:, xp.newaxis]/2 + knn_sim_bwd/2
+        if args.direction == 'forward':
+            src_indices = xp.arange(sim_size)
+            trg_indices = sim.argmax(axis=1)
+        elif args.direction == 'backward':
+            src_indices = sim.argmax(axis=0)
+            trg_indices = xp.arange(sim_size)
+        elif args.direction == 'union':
+            src_indices = xp.concatenate((xp.arange(sim_size), sim.argmax(axis=0)))
+            trg_indices = xp.concatenate((sim.argmax(axis=1), xp.arange(sim_size)))
+        del xsim, zsim, sim
+    elif args.init_numerals:
+        numeral_regex = re.compile('^[0-9]+$')
+        src_numerals = {word for word in src_words if numeral_regex.match(word) is not None}
+        trg_numerals = {word for word in trg_words if numeral_regex.match(word) is not None}
+        numerals = src_numerals.intersection(trg_numerals)
+        for word in numerals:
+            src_indices.append(src_word2ind[word])
+            trg_indices.append(trg_word2ind[word])
+    elif args.init_identical:
+        identical = set(src_words).intersection(set(trg_words))
+        for word in identical:
+            src_indices.append(src_word2ind[word])
+            trg_indices.append(trg_word2ind[word])
+    else:
+        f = open(args.init_dictionary, encoding=args.encoding, errors='surrogateescape')
+        for line in f:
+            src, trg = line.split()
+            try:
+                src_ind = src_word2ind[src]
+                trg_ind = trg_word2ind[trg]
+                src_indices.append(src_ind)
+                trg_indices.append(trg_ind)
+            except KeyError:
+                print('WARNING: OOV dictionary entry ({0} - {1})'.format(src, trg), file=sys.stderr)
+
+    # Read validation dictionary
+    if args.validation is not None:
+        f = open(args.validation, encoding=args.encoding, errors='surrogateescape')
+        validation = collections.defaultdict(set)
+        oov = set()
+        vocab = set()
+        for line in f:
+            src, trg = line.split()
+            try:
+                src_ind = src_word2ind[src]
+                trg_ind = trg_word2ind[trg]
+                validation[src_ind].add(trg_ind)
+                vocab.add(src)
+            except KeyError:
+                oov.add(src)
+        oov -= vocab  # If one of the translation options is in the vocabulary, then the entry is not an oov
+        validation_coverage = len(validation) / (len(validation) + len(oov))
+
+    # Create log file
+    if args.log:
+        log = open(args.log, mode='w', encoding=args.encoding, errors='surrogateescape')
+
+    # Allocate memory
+    xw = xp.empty_like(x)
+    zw = xp.empty_like(z)
+    src_size = x.shape[0] if args.vocabulary_cutoff <= 0 else min(x.shape[0], args.vocabulary_cutoff)
+    trg_size = z.shape[0] if args.vocabulary_cutoff <= 0 else min(z.shape[0], args.vocabulary_cutoff)
+    simfwd = xp.empty((args.batch_size, trg_size), dtype=dtype)
+    simbwd = xp.empty((args.batch_size, src_size), dtype=dtype)
+    if args.validation is not None:
+        simval = xp.empty((len(validation.keys()), z.shape[0]), dtype=dtype)
+
+    best_sim_forward = xp.full(src_size, -100, dtype=dtype)
+    src_indices_forward = xp.arange(src_size)
+    trg_indices_forward = xp.zeros(src_size, dtype=int)
+    best_sim_backward = xp.full(trg_size, -100, dtype=dtype)
+    src_indices_backward = xp.zeros(trg_size, dtype=int)
+    trg_indices_backward = xp.arange(trg_size)
+    knn_sim_fwd = xp.zeros(src_size, dtype=dtype)
+    knn_sim_bwd = xp.zeros(trg_size, dtype=dtype)
+
+    # Training loop
+    best_objective = objective = -100.
+    it = 1
+    last_improvement = 0
+    keep_prob = args.stochastic_initial
+    t = time.time()
+    end = not args.self_learning
+    while True:
+
+        # Increase the keep probability if we have not improve in args.stochastic_interval iterations
+        if it - last_improvement > args.stochastic_interval:
+            if keep_prob >= 1.0:
+                end = True
+            keep_prob = min(1.0, args.stochastic_multiplier*keep_prob)
+            last_improvement = it
+
+        # Update the embedding mapping
+        if args.orthogonal or not end:  # orthogonal mapping
+            u, s, vt = xp.linalg.svd(z[trg_indices].T.dot(x[src_indices]))
+            w = vt.T.dot(u.T)
+            x.dot(w, out=xw)
+            zw[:] = z
+        elif args.unconstrained:  # unconstrained mapping
+            x_pseudoinv = xp.linalg.inv(x[src_indices].T.dot(x[src_indices])).dot(x[src_indices].T)
+            w = x_pseudoinv.dot(z[trg_indices])
+            x.dot(w, out=xw)
+            zw[:] = z
+        else:  # advanced mapping
+
+            # TODO xw.dot(wx2, out=xw) and alike not working
+            xw[:] = x
+            zw[:] = z
+
+            # STEP 1: Whitening
+            def whitening_transformation(m):
+                u, s, vt = xp.linalg.svd(m, full_matrices=False)
+                return vt.T.dot(xp.diag(1/s)).dot(vt)
+            if args.whiten:
+                wx1 = whitening_transformation(xw[src_indices])
+                wz1 = whitening_transformation(zw[trg_indices])
+                xw = xw.dot(wx1)
+                zw = zw.dot(wz1)
+
+            # STEP 2: Orthogonal mapping
+            wx2, s, wz2_t = xp.linalg.svd(xw[src_indices].T.dot(zw[trg_indices]))
+            wz2 = wz2_t.T
+            xw = xw.dot(wx2)
+            zw = zw.dot(wz2)
+
+            # STEP 3: Re-weighting
+            xw *= s**args.src_reweight
+            zw *= s**args.trg_reweight
+
+            # STEP 4: De-whitening
+            if args.src_dewhiten == 'src':
+                xw = xw.dot(wx2.T.dot(xp.linalg.inv(wx1)).dot(wx2))
+            elif args.src_dewhiten == 'trg':
+                xw = xw.dot(wz2.T.dot(xp.linalg.inv(wz1)).dot(wz2))
+            if args.trg_dewhiten == 'src':
+                zw = zw.dot(wx2.T.dot(xp.linalg.inv(wx1)).dot(wx2))
+            elif args.trg_dewhiten == 'trg':
+                zw = zw.dot(wz2.T.dot(xp.linalg.inv(wz1)).dot(wz2))
+
+            # STEP 5: Dimensionality reduction
+            if args.dim_reduction > 0:
+                xw = xw[:, :args.dim_reduction]
+                zw = zw[:, :args.dim_reduction]
+
+        # Self-learning
+        if end:
+            break
+        else:
+            # Update the training dictionary
+            if args.direction in ('forward', 'union'):
+                if args.csls_neighborhood > 0:
+                    for i in range(0, trg_size, simbwd.shape[0]):
+                        j = min(i + simbwd.shape[0], trg_size)
+                        zw[i:j].dot(xw[:src_size].T, out=simbwd[:j-i])
+                        knn_sim_bwd[i:j] = topk_mean(simbwd[:j-i], k=args.csls_neighborhood, inplace=True)
+                for i in range(0, src_size, simfwd.shape[0]):
+                    j = min(i + simfwd.shape[0], src_size)
+                    xw[i:j].dot(zw[:trg_size].T, out=simfwd[:j-i])
+                    simfwd[:j-i].max(axis=1, out=best_sim_forward[i:j])
+                    simfwd[:j-i] -= knn_sim_bwd/2  # Equivalent to the real CSLS scores for NN
+                    dropout(simfwd[:j-i], 1 - keep_prob).argmax(axis=1, out=trg_indices_forward[i:j])
+            if args.direction in ('backward', 'union'):
+                if args.csls_neighborhood > 0:
+                    for i in range(0, src_size, simfwd.shape[0]):
+                        j = min(i + simfwd.shape[0], src_size)
+                        xw[i:j].dot(zw[:trg_size].T, out=simfwd[:j-i])
+                        knn_sim_fwd[i:j] = topk_mean(simfwd[:j-i], k=args.csls_neighborhood, inplace=True)
+                for i in range(0, trg_size, simbwd.shape[0]):
+                    j = min(i + simbwd.shape[0], trg_size)
+                    zw[i:j].dot(xw[:src_size].T, out=simbwd[:j-i])
+                    simbwd[:j-i].max(axis=1, out=best_sim_backward[i:j])
+                    simbwd[:j-i] -= knn_sim_fwd/2  # Equivalent to the real CSLS scores for NN
+                    dropout(simbwd[:j-i], 1 - keep_prob).argmax(axis=1, out=src_indices_backward[i:j])
+            if args.direction == 'forward':
+                src_indices = src_indices_forward
+                trg_indices = trg_indices_forward
+            elif args.direction == 'backward':
+                src_indices = src_indices_backward
+                trg_indices = trg_indices_backward
+            elif args.direction == 'union':
+                src_indices = xp.concatenate((src_indices_forward, src_indices_backward))
+                trg_indices = xp.concatenate((trg_indices_forward, trg_indices_backward))
+
+            # Objective function evaluation
+            if args.direction == 'forward':
+                objective = xp.mean(best_sim_forward).tolist()
+            elif args.direction == 'backward':
+                objective = xp.mean(best_sim_backward).tolist()
+            elif args.direction == 'union':
+                objective = (xp.mean(best_sim_forward) + xp.mean(best_sim_backward)).tolist() / 2
+            if objective - best_objective >= args.threshold:
+                last_improvement = it
+                best_objective = objective
+
+            # Accuracy and similarity evaluation in validation
+            if args.validation is not None:
+                src = list(validation.keys())
+                xw[src].dot(zw.T, out=simval)
+                nn = asnumpy(simval.argmax(axis=1))
+                accuracy = np.mean([1 if nn[i] in validation[src[i]] else 0 for i in range(len(src))])
+                similarity = np.mean([max([simval[i, j].tolist() for j in validation[src[i]]]) for i in range(len(src))])
+
+            # Logging
+            duration = time.time() - t
+            if args.verbose:
+                print(file=sys.stderr)
+                print('ITERATION {0} ({1:.2f}s)'.format(it, duration), file=sys.stderr)
+                print('\t- Objective:        {0:9.4f}%'.format(100 * objective), file=sys.stderr)
+                print('\t- Drop probability: {0:9.4f}%'.format(100 - 100*keep_prob), file=sys.stderr)
+                if args.validation is not None:
+                    print('\t- Val. similarity:  {0:9.4f}%'.format(100 * similarity), file=sys.stderr)
+                    print('\t- Val. accuracy:    {0:9.4f}%'.format(100 * accuracy), file=sys.stderr)
+                    print('\t- Val. coverage:    {0:9.4f}%'.format(100 * validation_coverage), file=sys.stderr)
+                sys.stderr.flush()
+            if args.log is not None:
+                val = '{0:.6f}\t{1:.6f}\t{2:.6f}'.format(
+                    100 * similarity, 100 * accuracy, 100 * validation_coverage) if args.validation is not None else ''
+                print('{0}\t{1:.6f}\t{2}\t{3:.6f}'.format(it, 100 * objective, val, duration), file=log)
+                log.flush()
+
+        t = time.time()
+        it += 1
+
+    # Write mapped embeddings
+    srcfile = open(args.src_output, mode='w', encoding=args.encoding, errors='surrogateescape')
+    trgfile = open(args.trg_output, mode='w', encoding=args.encoding, errors='surrogateescape')
+    embeddings.write(src_words, xw, srcfile)
+    embeddings.write(trg_words, zw, trgfile)
+    srcfile.close()
+    trgfile.close()
+
+
+if __name__ == '__main__':
+    main()