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|
"""
Prepares train, dev, test for a treebank
For example, do
python -m stanza.utils.datasets.prepare_tokenizer_treebank TREEBANK
such as
python -m stanza.utils.datasets.prepare_tokenizer_treebank UD_English-EWT
and it will prepare each of train, dev, test
There are macros for preparing all of the UD treebanks at once:
python -m stanza.utils.datasets.prepare_tokenizer_treebank ud_all
python -m stanza.utils.datasets.prepare_tokenizer_treebank all_ud
Both are present because I kept forgetting which was the correct one
There are a few special case handlings of treebanks in this file:
- all Vietnamese treebanks have special post-processing to handle
some of the difficult spacing issues in Vietnamese text
- treebanks with train and test but no dev split have the
train data randomly split into two pieces
- however, instead of splitting very tiny treebanks, we skip those
"""
import argparse
import glob
import os
import random
import re
import tempfile
from collections import Counter
from stanza.models.common.constant import treebank_to_short_name
import stanza.utils.datasets.common as common
from stanza.utils.datasets.common import read_sentences_from_conllu, write_sentences_to_conllu, INT_RE, MWT_RE, MWT_OR_COPY_RE
import stanza.utils.datasets.tokenization.convert_my_alt as convert_my_alt
import stanza.utils.datasets.tokenization.convert_vi_vlsp as convert_vi_vlsp
import stanza.utils.datasets.tokenization.convert_th_best as convert_th_best
import stanza.utils.datasets.tokenization.convert_th_lst20 as convert_th_lst20
import stanza.utils.datasets.tokenization.convert_th_orchid as convert_th_orchid
def copy_conllu_file(tokenizer_dir, tokenizer_file, dest_dir, dest_file, short_name):
original = f"{tokenizer_dir}/{short_name}.{tokenizer_file}.conllu"
copied = f"{dest_dir}/{short_name}.{dest_file}.conllu"
print("Copying from %s to %s" % (original, copied))
# do this instead of shutil.copyfile in case there are manipulations needed
sents = read_sentences_from_conllu(original)
write_sentences_to_conllu(copied, sents)
def copy_conllu_treebank(treebank, paths, dest_dir, postprocess=None, augment=True):
"""
This utility method copies only the conllu files to the given destination directory.
Both POS and lemma annotators need this.
"""
os.makedirs(dest_dir, exist_ok=True)
short_name = treebank_to_short_name(treebank)
short_language = short_name.split("_")[0]
with tempfile.TemporaryDirectory() as tokenizer_dir:
paths = dict(paths)
paths["TOKENIZE_DATA_DIR"] = tokenizer_dir
# first we process the tokenization data
args = argparse.Namespace()
args.augment = augment
args.prepare_labels = False
process_treebank(treebank, paths, args)
os.makedirs(dest_dir, exist_ok=True)
if postprocess is None:
postprocess = copy_conllu_file
# now we copy the processed conllu data files
postprocess(tokenizer_dir, "train.gold", dest_dir, "train.in", short_name)
postprocess(tokenizer_dir, "dev.gold", dest_dir, "dev.gold", short_name)
copy_conllu_file(dest_dir, "dev.gold", dest_dir, "dev.in", short_name)
postprocess(tokenizer_dir, "test.gold", dest_dir, "test.gold", short_name)
copy_conllu_file(dest_dir, "test.gold", dest_dir, "test.in", short_name)
def split_train_file(treebank, train_input_conllu, train_output_conllu, dev_output_conllu):
# set the seed for each data file so that the results are the same
# regardless of how many treebanks are processed at once
random.seed(1234)
# read and shuffle conllu data
sents = read_sentences_from_conllu(train_input_conllu)
random.shuffle(sents)
n_dev = int(len(sents) * XV_RATIO)
assert n_dev >= 1, "Dev sentence number less than one."
n_train = len(sents) - n_dev
# split conllu data
dev_sents = sents[:n_dev]
train_sents = sents[n_dev:]
print("Train/dev split not present. Randomly splitting train file from %s to %s and %s" % (train_input_conllu, train_output_conllu, dev_output_conllu))
print(f"{len(sents)} total sentences found: {n_train} in train, {n_dev} in dev")
# write conllu
write_sentences_to_conllu(train_output_conllu, train_sents)
write_sentences_to_conllu(dev_output_conllu, dev_sents)
return True
def strip_mwt_from_sentences(sents):
"""
Removes all mwt lines from the given list of sentences
Useful for mixing MWT and non-MWT treebanks together (especially English)
"""
new_sents = []
for sentence in sents:
new_sentence = [line for line in sentence if not MWT_RE.match(line)]
new_sents.append(new_sentence)
return new_sents
def has_space_after_no(piece):
if not piece or piece == "_":
return False
if piece == "SpaceAfter=No":
return True
tags = piece.split("|")
return any(t == "SpaceAfter=No" for t in tags)
def remove_space_after_no(piece, fail_if_missing=True):
"""
Removes a SpaceAfter=No annotation from a single piece of a single word.
In other words, given a list of conll lines, first call split("\t"), then call this on the -1 column
"""
# |SpaceAfter is in UD_Romanian-Nonstandard... seems fitting
if piece == "SpaceAfter=No" or piece == "|SpaceAfter=No":
piece = "_"
elif piece.startswith("SpaceAfter=No|"):
piece = piece.replace("SpaceAfter=No|", "")
elif piece.find("|SpaceAfter=No") > 0:
piece = piece.replace("|SpaceAfter=No", "")
elif fail_if_missing:
raise ValueError("Could not find SpaceAfter=No in the given notes field")
return piece
def add_space_after_no(piece, fail_if_found=True):
if piece == '_':
return "SpaceAfter=No"
else:
if fail_if_found:
if has_space_after_no(piece):
raise ValueError("Given notes field already contained SpaceAfter=No")
return piece + "|SpaceAfter=No"
def augment_arabic_padt(sents, ratio=0.05):
"""
Basic Arabic tokenizer gets the trailing punctuation wrong if there is a blank space.
Reason seems to be that there are almost no examples of "text ." in the dataset.
This function augments the Arabic-PADT dataset with a few such examples.
TODO: it may very well be that a lot of tokeners have this problem.
Also, there are a few examples in UD2.7 which are apparently
headlines where there is a ' . ' in the middle of the text.
According to an Arabic speaking labmate, the sentences are
headlines which could be reasonably split into two items. Having
them as one item is quite confusing and possibly incorrect, but
such is life.
"""
new_sents = []
for sentence in sents:
if len(sentence) < 4:
raise ValueError("Read a surprisingly short sentence")
text_line = None
if sentence[0].startswith("# newdoc") and sentence[3].startswith("# text"):
text_line = 3
elif sentence[0].startswith("# newpar") and sentence[2].startswith("# text"):
text_line = 2
elif sentence[0].startswith("# sent_id") and sentence[1].startswith("# text"):
text_line = 1
else:
raise ValueError("Could not find text line in %s" % sentence[0].split()[-1])
# for some reason performance starts dropping quickly at higher numbers
if random.random() > ratio:
continue
if (sentence[text_line][-1] in ('.', '؟', '?', '!') and
sentence[text_line][-2] not in ('.', '؟', '?', '!', ' ') and
has_space_after_no(sentence[-2].split()[-1]) and
len(sentence[-1].split()[1]) == 1):
new_sent = list(sentence)
new_sent[text_line] = new_sent[text_line][:-1] + ' ' + new_sent[text_line][-1]
pieces = sentence[-2].split("\t")
pieces[-1] = remove_space_after_no(pieces[-1])
new_sent[-2] = "\t".join(pieces)
assert new_sent != sentence
new_sents.append(new_sent)
return sents + new_sents
def augment_telugu(sents):
"""
Add a few sentences with modified punctuation to Telugu_MTG
The Telugu-MTG dataset has punctuation separated from the text in
almost all cases, which makes the tokenizer not learn how to
process that correctly.
All of the Telugu sentences end with their sentence final
punctuation being separated. Furthermore, all commas are
separated. We change that on some subset of the sentences to
make the tools more generalizable on wild text.
"""
new_sents = []
for sentence in sents:
if not sentence[1].startswith("# text"):
raise ValueError("Expected the second line of %s to start with # text" % sentence[0])
if not sentence[2].startswith("# translit"):
raise ValueError("Expected the second line of %s to start with # translit" % sentence[0])
if sentence[1].endswith(". . .") or sentence[1][-1] not in ('.', '?', '!'):
continue
if sentence[1][-1] in ('.', '?', '!') and sentence[1][-2] != ' ' and sentence[1][-3:] != ' ..' and sentence[1][-4:] != ' ...':
raise ValueError("Sentence %s does not end with space-punctuation, which is against our assumptions for the te_mtg treebank. Please check the augment method to see if it is still needed" % sentence[0])
if random.random() < 0.1:
new_sentence = list(sentence)
new_sentence[1] = new_sentence[1][:-2] + new_sentence[1][-1]
new_sentence[2] = new_sentence[2][:-2] + new_sentence[2][-1]
new_sentence[-2] = new_sentence[-2] + "|SpaceAfter=No"
new_sents.append(new_sentence)
if sentence[1].find(",") > 1 and random.random() < 0.1:
new_sentence = list(sentence)
index = sentence[1].find(",")
new_sentence[1] = sentence[1][:index-1] + sentence[1][index:]
index = sentence[1].find(",")
new_sentence[2] = sentence[2][:index-1] + sentence[2][index:]
for idx, word in enumerate(new_sentence):
if idx < 4:
# skip sent_id, text, transliteration, and the first word
continue
if word.split("\t")[1] == ',':
new_sentence[idx-1] = new_sentence[idx-1] + "|SpaceAfter=No"
break
new_sents.append(new_sentence)
return sents + new_sents
COMMA_SEPARATED_RE = re.compile(" ([a-zA-Z]+)[,] ([a-zA-Z]+) ")
def augment_comma_separations(sents):
"""Find some fraction of the sentences which match "asdf, zzzz" and squish them to "asdf,zzzz"
This leaves the tokens and all of the other data the same. The
only change made is to change SpaceAfter=No for the "," token and
adjust the #text line, with the assumption that the conllu->txt
conversion will correctly handle this change.
This was particularly an issue for Spanish-AnCora, but it's
reasonable to think it could happen to any dataset. Currently
this just operates on commas and ascii letters to avoid
accidentally squishing anything that shouldn't be squished.
UD_Spanish-AnCora 2.7 had a problem is with this sentence:
# orig_file_sentence 143#5
In this sentence, there was a comma smashed next to a token.
Fixing just this one sentence is not sufficient to tokenize
"asdf,zzzz" as desired, so we also augment by some fraction where
we have squished "asdf, zzzz" into "asdf,zzzz".
This exact example was later fixed in UD 2.8, but it should still
potentially be useful for compensating for typos.
"""
new_sents = []
for sentence in sents:
for text_idx, text_line in enumerate(sentence):
# look for the line that starts with "# text".
# keep going until we find it, or silently ignore it
# if the dataset isn't in that format
if text_line.startswith("# text"):
break
else:
continue
match = COMMA_SEPARATED_RE.search(sentence[text_idx])
if match and random.random() < 0.03:
for idx, word in enumerate(sentence):
if word.startswith("#"):
continue
# find() doesn't work because we wind up finding substrings
if word.split("\t")[1] != match.group(1):
continue
if sentence[idx+1].split("\t")[1] != ',':
continue
if sentence[idx+2].split("\t")[2] != match.group(2):
continue
break
if idx == len(sentence) - 1:
# this can happen with MWTs. we may actually just
# want to skip MWTs anyway, so no big deal
continue
# now idx+1 should be the line with the comma in it
comma = sentence[idx+1]
pieces = comma.split("\t")
assert pieces[1] == ','
pieces[-1] = add_space_after_no(pieces[-1])
comma = "\t".join(pieces)
new_sent = sentence[:idx+1] + [comma] + sentence[idx+2:]
text_offset = sentence[text_idx].find(match.group(1) + ", " + match.group(2))
text_len = len(match.group(1) + ", " + match.group(2))
new_text = sentence[text_idx][:text_offset] + match.group(1) + "," + match.group(2) + sentence[text_idx][text_offset+text_len:]
new_sent[text_idx] = new_text
new_sents.append(new_sent)
print("Added %d new sentences with asdf, zzzz -> asdf,zzzz" % len(new_sents))
return sents + new_sents
def augment_move_comma(sents, ratio=0.02):
"""
Move the comma from after a word to before the next word some fraction of the time
We looks for this exact pattern:
w1, w2
and replace it with
w1 ,w2
The idea is that this is a relatively common typo, but the tool
won't learn how to tokenize it without some help.
Note that this modification replaces the original text.
"""
new_sents = []
num_operations = 0
for sentence in sents:
if random.random() > ratio:
new_sents.append(sentence)
continue
found = False
for word_idx, word in enumerate(sentence):
if word.startswith("#"):
continue
if word_idx == 0 or word_idx >= len(sentence) - 2:
continue
pieces = word.split("\t")
if pieces[1] == ',' and not has_space_after_no(pieces[-1]):
# found a comma with a space after it
prev_word = sentence[word_idx-1]
if not has_space_after_no(prev_word.split("\t")[-1]):
# unfortunately, the previous word also had a
# space after it. does not fit what we are
# looking for
continue
# also, want to skip instances near MWT or copy nodes,
# since those are harder to rearrange
next_word = sentence[word_idx+1]
if MWT_OR_COPY_RE.match(next_word.split("\t")[0]):
continue
if MWT_OR_COPY_RE.match(prev_word.split("\t")[0]):
continue
# at this point, the previous word has no space and the comma does
found = True
break
if not found:
new_sents.append(sentence)
continue
new_sentence = list(sentence)
pieces = new_sentence[word_idx].split("\t")
pieces[-1] = add_space_after_no(pieces[-1])
new_sentence[word_idx] = "\t".join(pieces)
pieces = new_sentence[word_idx-1].split("\t")
prev_word = pieces[1]
pieces[-1] = remove_space_after_no(pieces[-1])
new_sentence[word_idx-1] = "\t".join(pieces)
next_word = new_sentence[word_idx+1].split("\t")[1]
for text_idx, text_line in enumerate(sentence):
# look for the line that starts with "# text".
# keep going until we find it, or silently ignore it
# if the dataset isn't in that format
if text_line.startswith("# text"):
old_chunk = prev_word + ", " + next_word
new_chunk = prev_word + " ," + next_word
word_idx = text_line.find(old_chunk)
if word_idx < 0:
raise RuntimeError("Unexpected #text line which did not contain the original text to be modified. Looking for\n" + old_chunk + "\n" + text_line)
new_text_line = text_line[:word_idx] + new_chunk + text_line[word_idx+len(old_chunk):]
new_sentence[text_idx] = new_text_line
break
new_sents.append(new_sentence)
num_operations = num_operations + 1
print("Swapped 'w1, w2' for 'w1 ,w2' %d times" % num_operations)
return new_sents
def augment_apos(sents):
"""
If there are no instances of ’ in the dataset, but there are instances of ',
we replace some fraction of ' with ’ so that the tokenizer will recognize it.
"""
has_unicode_apos = False
has_ascii_apos = False
for sent in sents:
for line in sent:
if line.startswith("# text"):
if line.find("'") >= 0:
has_ascii_apos = True
if line.find("’") >= 0:
has_unicode_apos = True
break
else:
raise ValueError("Cannot find '# text'")
if has_unicode_apos or not has_ascii_apos:
return sents
new_sents = []
for sent in sents:
if random.random() > 0.05:
new_sents.append(sent)
continue
new_sent = []
for line in sent:
if line.startswith("# text"):
new_sent.append(line.replace("'", "’"))
elif line.startswith("#"):
new_sent.append(line)
else:
pieces = line.split("\t")
pieces[1] = pieces[1].replace("'", "’")
new_sent.append("\t".join(pieces))
new_sents.append(new_sent)
return new_sents
def augment_ellipses(sents):
"""
Replaces a fraction of '...' with '…'
"""
has_ellipses = False
has_unicode_ellipses = False
for sent in sents:
for line in sent:
if line.startswith("#"):
continue
pieces = line.split("\t")
if pieces[1] == '...':
has_ellipses = True
elif pieces[1] == '…':
has_unicode_ellipses = True
if has_unicode_ellipses or not has_ellipses:
return sents
new_sents = []
for sent in sents:
if random.random() > 0.05:
new_sents.append(sent)
continue
new_sent = []
for line in sent:
if line.startswith("#"):
new_sent.append(line)
else:
pieces = line.split("\t")
if pieces[1] == '...':
pieces[1] = '…'
new_sent.append("\t".join(pieces))
new_sents.append(new_sent)
return new_sents
# https://en.wikipedia.org/wiki/Quotation_mark
QUOTES = ['"', '“', '”', '«', '»', '「', '」', '《', '》', '„', '″']
QUOTES_RE = re.compile("(.?)[" + "".join(QUOTES) + "](.+)[" + "".join(QUOTES) + "](.?)")
# Danish does '«' the other way around from most European languages
START_QUOTES = ['"', '“', '”', '«', '»', '「', '《', '„', '„', '″']
END_QUOTES = ['"', '“', '”', '»', '«', '」', '》', '”', '“', '″']
def augment_quotes(sents, ratio=0.15):
"""
Go through the sentences and replace a fraction of sentences with alternate quotes
TODO: for certain languages we may want to make some language-specific changes
eg Danish, don't add «...»
"""
assert len(START_QUOTES) == len(END_QUOTES)
counts = Counter()
new_sents = []
for sent in sents:
if random.random() > ratio:
new_sents.append(sent)
continue
# count if there are exactly 2 quotes in this sentence
# this is for convenience - otherwise we need to figure out which pairs go together
count_quotes = sum(1 for x in sent
if (not x.startswith("#") and
x.split("\t")[1] in QUOTES))
if count_quotes != 2:
new_sents.append(sent)
continue
# choose a pair of quotes from the candidates
quote_idx = random.choice(range(len(START_QUOTES)))
start_quote = START_QUOTES[quote_idx]
end_quote = END_QUOTES[quote_idx]
counts[start_quote + end_quote] = counts[start_quote + end_quote] + 1
new_sent = []
saw_start = False
for line in sent:
if line.startswith("#"):
new_sent.append(line)
continue
pieces = line.split("\t")
if pieces[1] in QUOTES:
if saw_start:
# Note that we don't change the lemma. Presumably it's
# set to the correct lemma for a quote for this treebank
pieces[1] = end_quote
else:
pieces[1] = start_quote
saw_start = True
new_sent.append("\t".join(pieces))
else:
new_sent.append(line)
for text_idx, text_line in enumerate(new_sent):
# look for the line that starts with "# text".
# keep going until we find it, or silently ignore it
# if the dataset isn't in that format
if text_line.startswith("# text"):
replacement = "\\1%s\\2%s\\3" % (start_quote, end_quote)
new_text_line = QUOTES_RE.sub(replacement, text_line)
new_sent[text_idx] = new_text_line
new_sents.append(new_sent)
print("Augmented {} quotes: {}".format(sum(counts.values()), counts))
return new_sents
def find_text_idx(sentence):
"""
Return the index of the # text line or -1
"""
for idx, line in enumerate(sentence):
if line.startswith("# text"):
return idx
return -1
DIGIT_RE = re.compile("[0-9]")
def change_indices(line, delta):
"""
Adjust all indices in the given sentence by delta. Useful when removing a word, for example
"""
if line.startswith("#"):
return line
pieces = line.split("\t")
if MWT_RE.match(pieces[0]):
indices = pieces[0].split("-")
pieces[0] = "%d-%d" % (int(indices[0]) + delta, int(indices[1]) + delta)
line = "\t".join(pieces)
return line
if MWT_OR_COPY_RE.match(pieces[0]):
index_pieces = pieces[0].split(".", maxsplit=1)
pieces[0] = "%d.%s" % (int(index_pieces[0]) + delta, index_pieces[1])
elif not INT_RE.match(pieces[0]):
raise NotImplementedError("Unknown index type: %s" % pieces[0])
else:
pieces[0] = str(int(pieces[0]) + delta)
if pieces[6] != '_':
# copy nodes don't have basic dependencies in the es_ancora treebank
dep = int(pieces[6])
if dep != 0:
pieces[6] = str(int(dep) + delta)
if pieces[8] != '_':
dep_pieces = pieces[8].split(":", maxsplit=1)
if DIGIT_RE.search(dep_pieces[1]):
raise NotImplementedError("Need to handle multiple additional deps:\n%s" % line)
if int(dep_pieces[0]) != 0:
pieces[8] = str(int(dep_pieces[0]) + delta) + ":" + dep_pieces[1]
line = "\t".join(pieces)
return line
def augment_initial_punct(sents, ratio=0.20):
"""
If a sentence starts with certain punct marks, occasionally use the same sentence without the initial punct.
Currently this just handles ¿
This helps languages such as CA and ES where the models go awry when the initial ¿ is missing.
"""
new_sents = []
for sent in sents:
if random.random() > ratio:
continue
text_idx = find_text_idx(sent)
text_line = sent[text_idx]
if text_line.count("¿") != 1:
# only handle sentences with exactly one ¿
continue
# find the first line with actual text
for idx, line in enumerate(sent):
if line.startswith("#"):
continue
break
if idx >= len(sent) - 1:
raise ValueError("Unexpectedly an entire sentence is comments")
pieces = line.split("\t")
if pieces[1] != '¿':
continue
if has_space_after_no(pieces[-1]):
replace_text = "¿"
else:
replace_text = "¿ "
new_sent = sent[:idx] + sent[idx+1:]
new_sent[text_idx] = text_line.replace(replace_text, "")
# now need to update all indices
new_sent = [change_indices(x, -1) for x in new_sent]
new_sents.append(new_sent)
if len(new_sents) > 0:
print("Added %d sentences with the leading ¿ removed" % len(new_sents))
return sents + new_sents
def augment_punct(sents):
"""
If there are no instances of ’ in the dataset, but there are instances of ',
we replace some fraction of ' with ’ so that the tokenizer will recognize it.
Also augments with ... / …
"""
new_sents = augment_apos(sents)
new_sents = augment_quotes(new_sents)
new_sents = augment_move_comma(new_sents)
new_sents = augment_comma_separations(new_sents)
new_sents = augment_initial_punct(new_sents)
new_sents = augment_ellipses(new_sents)
return new_sents
def write_augmented_dataset(input_conllu, output_conllu, augment_function):
# set the seed for each data file so that the results are the same
# regardless of how many treebanks are processed at once
random.seed(1234)
# read and shuffle conllu data
sents = read_sentences_from_conllu(input_conllu)
# the actual meat of the function - produce new sentences
new_sents = augment_function(sents)
write_sentences_to_conllu(output_conllu, new_sents)
def remove_spaces_from_sentences(sents):
"""
Makes sure every word in the list of sentences has SpaceAfter=No.
Returns a new list of sentences
"""
new_sents = []
for sentence in sents:
new_sentence = []
for word in sentence:
if word.startswith("#"):
new_sentence.append(word)
continue
pieces = word.split("\t")
if pieces[-1] == "_":
pieces[-1] = "SpaceAfter=No"
elif pieces[-1].find("SpaceAfter=No") >= 0:
pass
else:
raise ValueError("oops")
word = "\t".join(pieces)
new_sentence.append(word)
new_sents.append(new_sentence)
return new_sents
def remove_spaces(input_conllu, output_conllu):
"""
Turns a dataset into something appropriate for building a segmenter.
For example, this works well on the Korean datasets.
"""
sents = read_sentences_from_conllu(input_conllu)
new_sents = remove_spaces_from_sentences(sents)
write_sentences_to_conllu(output_conllu, new_sents)
def build_combined_korean_dataset(udbase_dir, tokenizer_dir, short_name, dataset, output_conllu):
"""
Builds a combined dataset out of multiple Korean datasets.
Currently this uses GSD and Kaist. If a segmenter-appropriate
dataset was requested, spaces are removed.
TODO: we need to handle the difference in xpos tags somehow.
"""
gsd_conllu = common.find_treebank_dataset_file("UD_Korean-GSD", udbase_dir, dataset, "conllu")
kaist_conllu = common.find_treebank_dataset_file("UD_Korean-Kaist", udbase_dir, dataset, "conllu")
sents = read_sentences_from_conllu(gsd_conllu) + read_sentences_from_conllu(kaist_conllu)
segmenter = short_name.endswith("_seg")
if segmenter:
sents = remove_spaces_from_sentences(sents)
write_sentences_to_conllu(output_conllu, sents)
def build_combined_korean(udbase_dir, tokenizer_dir, short_name):
for dataset in ("train", "dev", "test"):
output_conllu = common.tokenizer_conllu_name(tokenizer_dir, short_name, dataset)
build_combined_korean_dataset(udbase_dir, tokenizer_dir, short_name, dataset, output_conllu)
def build_combined_italian_dataset(paths, dataset):
udbase_dir = paths["UDBASE"]
if dataset == 'train':
# could maybe add ParTUT, but that dataset has a slightly different xpos set
# (no DE or I)
# and I didn't feel like sorting through the differences
# Note: currently these each have small changes compared with
# the UD2.7 release. See the issues (possibly closed by now)
# filed by AngledLuffa on each of the treebanks for more info.
treebanks = ["UD_Italian-ISDT", "UD_Italian-VIT", "UD_Italian-TWITTIRO", "UD_Italian-PoSTWITA"]
sents = []
for treebank in treebanks:
conllu_file = common.find_treebank_dataset_file(treebank, udbase_dir, dataset, "conllu", fail=True)
sents.extend(read_sentences_from_conllu(conllu_file))
else:
istd_conllu = common.find_treebank_dataset_file("UD_Italian-ISDT", udbase_dir, dataset, "conllu")
sents = read_sentences_from_conllu(istd_conllu)
return sents
def check_gum_ready(udbase_dir):
gum_conllu = common.find_treebank_dataset_file("UD_English-GUMReddit", udbase_dir, "train", "conllu")
if common.mostly_underscores(gum_conllu):
raise ValueError("Cannot process UD_English-GUMReddit in its current form. There should be a download script available in the directory which will help integrate the missing proprietary values. Please run that script to update the data, then try again.")
def build_combined_english_dataset(paths, dataset):
"""
en_combined is currently EWT, GUM, PUD, Pronouns, and handparsed
"""
udbase_dir = paths["UDBASE"]
check_gum_ready(udbase_dir)
if dataset == 'train':
# TODO: include more UD treebanks, possibly with xpos removed
# UD_English-ParTUT - xpos are different
# also include "external" treebanks such as PTB
# NOTE: in order to get the best results, make sure each of these treebanks have the latest edits applied
train_treebanks = ["UD_English-EWT", "UD_English-GUM", "UD_English-GUMReddit"]
test_treebanks = ["UD_English-PUD", "UD_English-Pronouns"]
sents = []
for treebank in train_treebanks:
conllu_file = common.find_treebank_dataset_file(treebank, udbase_dir, "train", "conllu", fail=True)
new_sents = read_sentences_from_conllu(conllu_file)
print("Read %d sentences from %s" % (len(new_sents), conllu_file))
sents.extend(new_sents)
for treebank in test_treebanks:
conllu_file = common.find_treebank_dataset_file(treebank, udbase_dir, "test", "conllu", fail=True)
new_sents = read_sentences_from_conllu(conllu_file)
print("Read %d sentences from %s" % (len(new_sents), conllu_file))
sents.extend(new_sents)
else:
ewt_conllu = common.find_treebank_dataset_file("UD_English-EWT", udbase_dir, dataset, "conllu")
sents = read_sentences_from_conllu(ewt_conllu)
sents = strip_mwt_from_sentences(sents)
return sents
def build_extra_combined_english_dataset(paths, dataset):
"""
Extra sentences we don't want augmented
"""
handparsed_dir = paths["HANDPARSED_DIR"]
sents = []
if dataset == 'train':
sents.extend(read_sentences_from_conllu(os.path.join(handparsed_dir, "english-handparsed", "english.conll")))
return sents
def build_extra_combined_italian_dataset(paths, dataset):
"""
Extra data - the MWT data for Italian
"""
handparsed_dir = paths["HANDPARSED_DIR"]
if dataset != 'train':
return []
extra_italian = os.path.join(handparsed_dir, "italian-mwt", "italian.mwt")
if not os.path.exists(extra_italian):
raise FileNotFoundError("Cannot find the extra dataset 'italian.mwt' which includes various multi-words retokenized, expected {}".format(extra_italian))
extra_sents = read_sentences_from_conllu(extra_italian)
for sentence in extra_sents:
if not sentence[2].endswith("_") or not MWT_RE.match(sentence[2]):
raise AssertionError("Unexpected format of the italian.mwt file. Has it already be modified to have SpaceAfter=No everywhere?")
sentence[2] = sentence[2][:-1] + "SpaceAfter=No"
return extra_sents
def replace_semicolons(sentences):
"""
Spanish GSD and AnCora have different standards for semicolons.
GSD has semicolons at the end of sentences, AnCora has them in the middle as clause separators.
Consecutive sentences in GSD do not seem to be related, so there is no combining that can be done.
The easiest solution is to replace sentence final semicolons with "." in GSD
"""
new_sents = []
count = 0
for sentence in sentences:
for text_idx, text_line in enumerate(sentence):
if text_line.startswith("# text"):
break
else:
raise ValueError("Expected every sentence in GSD to have a # text field")
if not text_line.endswith(";"):
new_sents.append(sentence)
continue
count = count + 1
new_sent = list(sentence)
new_sent[text_idx] = text_line[:-1] + "."
new_sent[-1] = new_sent[-1].replace(";", ".")
count = count + 1
new_sents.append(new_sent)
print("Updated %d sentences to replace sentence-final ; with ." % count)
return new_sents
def build_combined_spanish_dataset(paths, dataset):
"""
es_combined is AnCora and GSD put together
TODO: remove features which aren't shared between datasets
TODO: consider mixing in PUD?
"""
udbase_dir = paths["UDBASE"]
tokenizer_dir = paths["TOKENIZE_DATA_DIR"]
handparsed_dir = paths["HANDPARSED_DIR"]
if dataset == 'train':
treebanks = ["UD_Spanish-AnCora", "UD_Spanish-GSD"]
sents = []
for treebank in treebanks:
conllu_file = common.find_treebank_dataset_file(treebank, udbase_dir, dataset, "conllu", fail=True)
new_sents = read_sentences_from_conllu(conllu_file)
if treebank.endswith("GSD"):
new_sents = replace_semicolons(new_sents)
sents.extend(new_sents)
extra_spanish = os.path.join(handparsed_dir, "spanish-mwt", "spanish.mwt")
if not os.path.exists(extra_spanish):
raise FileNotFoundError("Cannot find the extra dataset 'spanish.mwt' which includes various multi-words retokenized, expected {}".format(extra_italian))
extra_sents = read_sentences_from_conllu(extra_spanish)
sents.extend(extra_sents)
else:
conllu_file = common.find_treebank_dataset_file("UD_Spanish-AnCora", udbase_dir, dataset, "conllu", fail=True)
sents = read_sentences_from_conllu(conllu_file)
return sents
def build_combined_hebrew_dataset(paths, dataset):
"""
Combines the IAHLT treebank with an updated form of HTB where the annotation style more closes matches IAHLT
Currently the updated HTB is not in UD, so you will need to clone
git@github.com:IAHLT/UD_Hebrew.git to $UDBASE_GIT
dev and test sets will be those from IAHLT
"""
udbase_dir = paths["UDBASE"]
udbase_git_dir = paths["UDBASE_GIT"]
treebanks = ["UD_Hebrew-IAHLTwiki"]
if dataset == 'train':
sents = []
for treebank in treebanks:
conllu_file = common.find_treebank_dataset_file(treebank, udbase_dir, dataset, "conllu", fail=True)
new_sents = read_sentences_from_conllu(conllu_file)
print("Read %d sentences from %s" % (len(new_sents), conllu_file))
sents.extend(new_sents)
# if/when this gets ported back to UD, switch to getting both datasets from UD
hebrew_git_dir = os.path.join(udbase_git_dir, "UD_Hebrew")
if not os.path.exists(hebrew_git_dir):
raise FileNotFoundError("Please download git@github.com:IAHLT/UD_Hebrew.git to %s (based on $UDBASE_GIT)" % hebrew_git_dir)
conllu_file = os.path.join(hebrew_git_dir, "he_htb-ud-train.conllu")
if not os.path.exists(conllu_file):
raise FileNotFoundError("Found %s but inexplicably there was no %s" % (hebrew_git_dir, conllu_file))
new_sents = read_sentences_from_conllu(conllu_file)
print("Read %d sentences from %s" % (len(new_sents), conllu_file))
sents.extend(new_sents)
else:
conllu_file = common.find_treebank_dataset_file(treebanks[0], udbase_dir, dataset, "conllu", fail=True)
sents = read_sentences_from_conllu(conllu_file)
return sents
COMBINED_FNS = {
"en_combined": build_combined_english_dataset,
"es_combined": build_combined_spanish_dataset,
"he_combined": build_combined_hebrew_dataset,
"it_combined": build_combined_italian_dataset,
}
# some extra data for the combined models without augmenting
COMBINED_EXTRA_FNS = {
"en_combined": build_extra_combined_english_dataset,
"it_combined": build_extra_combined_italian_dataset,
}
def build_combined_dataset(paths, short_name, augment):
random.seed(1234)
tokenizer_dir = paths["TOKENIZE_DATA_DIR"]
build_fn = COMBINED_FNS[short_name]
extra_fn = COMBINED_EXTRA_FNS.get(short_name, None)
for dataset in ("train", "dev", "test"):
output_conllu = common.tokenizer_conllu_name(tokenizer_dir, short_name, dataset)
sents = build_fn(paths, dataset)
if dataset == 'train' and augment:
sents = augment_punct(sents)
if extra_fn is not None:
sents.extend(extra_fn(paths, dataset))
write_sentences_to_conllu(output_conllu, sents)
BIO_DATASETS = ("en_craft", "en_genia", "en_mimic")
def build_bio_dataset(paths, udbase_dir, tokenizer_dir, handparsed_dir, short_name, augment):
"""
Process the en bio datasets
Creates a dataset by combining the en_combined data with one of the bio sets
"""
random.seed(1234)
name, bio_dataset = short_name.split("_")
assert name == 'en'
for dataset in ("train", "dev", "test"):
output_conllu = common.tokenizer_conllu_name(tokenizer_dir, short_name, dataset)
if dataset == 'train':
sents = build_combined_english_dataset(paths, dataset)
if dataset == 'train' and augment:
sents = augment_punct(sents)
else:
sents = []
bio_file = os.path.join(paths["BIO_UD_DIR"], "UD_English-%s" % bio_dataset.upper(), "en_%s-ud-%s.conllu" % (bio_dataset.lower(), dataset))
sents.extend(read_sentences_from_conllu(bio_file))
write_sentences_to_conllu(output_conllu, sents)
def build_combined_english_gum_dataset(udbase_dir, tokenizer_dir, short_name, dataset, augment):
"""
Build the GUM dataset by combining GUMReddit
It checks to make sure GUMReddit is filled out using the included script
"""
check_gum_ready(udbase_dir)
random.seed(1234)
output_conllu = common.tokenizer_conllu_name(tokenizer_dir, short_name, dataset)
treebanks = ["UD_English-GUM", "UD_English-GUMReddit"]
sents = []
for treebank in treebanks:
conllu_file = common.find_treebank_dataset_file(treebank, udbase_dir, dataset, "conllu", fail=True)
sents.extend(read_sentences_from_conllu(conllu_file))
if dataset == 'train' and augment:
sents = augment_punct(sents)
write_sentences_to_conllu(output_conllu, sents)
def build_combined_english_gum(udbase_dir, tokenizer_dir, short_name, augment):
for dataset in ("train", "dev", "test"):
build_combined_english_gum_dataset(udbase_dir, tokenizer_dir, short_name, dataset, augment)
def prepare_ud_dataset(treebank, udbase_dir, tokenizer_dir, short_name, short_language, dataset, augment=True, input_conllu=None, output_conllu=None):
if input_conllu is None:
input_conllu = common.find_treebank_dataset_file(treebank, udbase_dir, dataset, "conllu", fail=True)
if output_conllu is None:
output_conllu = common.tokenizer_conllu_name(tokenizer_dir, short_name, dataset)
print("Reading from %s and writing to %s" % (input_conllu, output_conllu))
if short_name == "te_mtg" and dataset == 'train' and augment:
write_augmented_dataset(input_conllu, output_conllu, augment_telugu)
elif short_name == "ar_padt" and dataset == 'train' and augment:
write_augmented_dataset(input_conllu, output_conllu, augment_arabic_padt)
elif short_name.startswith("ko_") and short_name.endswith("_seg"):
remove_spaces(input_conllu, output_conllu)
elif dataset == 'train' and augment:
write_augmented_dataset(input_conllu, output_conllu, augment_punct)
else:
sents = read_sentences_from_conllu(input_conllu)
write_sentences_to_conllu(output_conllu, sents)
def process_ud_treebank(treebank, udbase_dir, tokenizer_dir, short_name, short_language, augment=True):
"""
Process a normal UD treebank with train/dev/test splits
SL-SSJ and other datasets with inline modifications all use this code path as well.
"""
prepare_ud_dataset(treebank, udbase_dir, tokenizer_dir, short_name, short_language, "train", augment)
prepare_ud_dataset(treebank, udbase_dir, tokenizer_dir, short_name, short_language, "dev", augment)
prepare_ud_dataset(treebank, udbase_dir, tokenizer_dir, short_name, short_language, "test", augment)
XV_RATIO = 0.2
def process_partial_ud_treebank(treebank, udbase_dir, tokenizer_dir, short_name, short_language):
"""
Process a UD treebank with only train/test splits
For example, in UD 2.7:
UD_Buryat-BDT
UD_Galician-TreeGal
UD_Indonesian-CSUI
UD_Kazakh-KTB
UD_Kurmanji-MG
UD_Latin-Perseus
UD_Livvi-KKPP
UD_North_Sami-Giella
UD_Old_Russian-RNC
UD_Sanskrit-Vedic
UD_Slovenian-SST
UD_Upper_Sorbian-UFAL
UD_Welsh-CCG
"""
train_input_conllu = common.find_treebank_dataset_file(treebank, udbase_dir, "train", "conllu")
test_input_conllu = common.find_treebank_dataset_file(treebank, udbase_dir, "test", "conllu")
train_output_conllu = common.tokenizer_conllu_name(tokenizer_dir, short_name, "train")
dev_output_conllu = common.tokenizer_conllu_name(tokenizer_dir, short_name, "dev")
test_output_conllu = common.tokenizer_conllu_name(tokenizer_dir, short_name, "test")
if (common.num_words_in_file(train_input_conllu) <= 1000 and
common.num_words_in_file(test_input_conllu) > 5000):
train_input_conllu, test_input_conllu = test_input_conllu, train_input_conllu
if not split_train_file(treebank=treebank,
train_input_conllu=train_input_conllu,
train_output_conllu=train_output_conllu,
dev_output_conllu=dev_output_conllu):
return
# the test set is already fine
# currently we do not do any augmentation of these partial treebanks
prepare_ud_dataset(treebank, udbase_dir, tokenizer_dir, short_name, short_language, "test", augment=False, input_conllu=test_input_conllu, output_conllu=test_output_conllu)
def add_specific_args(parser):
parser.add_argument('--no_augment', action='store_false', dest='augment', default=True,
help='Augment the dataset in various ways')
parser.add_argument('--no_prepare_labels', action='store_false', dest='prepare_labels', default=True,
help='Prepare tokenizer and MWT labels. Expensive, but obviously necessary for training those models.')
convert_th_lst20.add_lst20_args(parser)
convert_vi_vlsp.add_vlsp_args(parser)
def process_treebank(treebank, paths, args):
"""
Processes a single treebank into train, dev, test parts
Includes processing for a few external tokenization datasets:
vi_vlsp, th_orchid, th_best
Also, there is no specific mechanism for UD_Arabic-NYUAD or
similar treebanks, which need integration with LDC datsets
"""
udbase_dir = paths["UDBASE"]
tokenizer_dir = paths["TOKENIZE_DATA_DIR"]
handparsed_dir = paths["HANDPARSED_DIR"]
short_name = treebank_to_short_name(treebank)
short_language = short_name.split("_")[0]
os.makedirs(tokenizer_dir, exist_ok=True)
if short_name == "my_alt":
convert_my_alt.convert_my_alt(paths["CONSTITUENCY_BASE"], tokenizer_dir)
elif short_name == "vi_vlsp":
convert_vi_vlsp.convert_vi_vlsp(paths["EXTERN_DIR"], tokenizer_dir, args)
elif short_name == "th_orchid":
convert_th_orchid.main(paths["EXTERN_DIR"], tokenizer_dir)
elif short_name == "th_lst20":
convert_th_lst20.convert(paths["EXTERN_DIR"], tokenizer_dir, args)
elif short_name == "th_best":
convert_th_best.main(paths["EXTERN_DIR"], tokenizer_dir)
elif short_name.startswith("ko_combined"):
build_combined_korean(udbase_dir, tokenizer_dir, short_name)
elif short_name in COMBINED_FNS: # eg "it_combined", "en_combined", etc
build_combined_dataset(paths, short_name, args.augment)
elif short_name in BIO_DATASETS:
build_bio_dataset(paths, udbase_dir, tokenizer_dir, handparsed_dir, short_name, args.augment)
elif short_name.startswith("en_gum"):
# we special case GUM because it should include a filled-out GUMReddit
print("Preparing data for %s: %s, %s" % (treebank, short_name, short_language))
build_combined_english_gum(udbase_dir, tokenizer_dir, short_name, args.augment)
else:
# check that we can find the train file where we expect it
train_conllu_file = common.find_treebank_dataset_file(treebank, udbase_dir, "train", "conllu", fail=True)
print("Preparing data for %s: %s, %s" % (treebank, short_name, short_language))
if not common.find_treebank_dataset_file(treebank, udbase_dir, "dev", "conllu", fail=False):
process_partial_ud_treebank(treebank, udbase_dir, tokenizer_dir, short_name, short_language)
else:
process_ud_treebank(treebank, udbase_dir, tokenizer_dir, short_name, short_language, args.augment)
if not short_name in ('th_orchid', 'th_lst20'):
common.convert_conllu_to_txt(tokenizer_dir, short_name)
if args.prepare_labels:
common.prepare_tokenizer_treebank_labels(tokenizer_dir, short_name)
def main():
common.main(process_treebank, add_specific_args)
if __name__ == '__main__':
main()
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