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#!/usr/bin/env python
# Author: Phil Williams
# Usage: filter-rule-table.py [--min-non-initial-rule-count=N] INPUT
#
# Given a rule table (on stdin) and an input text, filter out rules that
# couldn't be used in parsing the input and write the resulting rule table
# to stdout. The input text is assumed to contain the same factors as
# the rule table and is assumed to be small (not more than a few thousand
# sentences): the current algorithm won't scale well to large input sets.
#
# The filtering algorithm considers a source RHS to be a sequence of
# words and gaps, which must match a sequence of words in one of the
# input sentences, with at least one input word per gap. The NT labels
# are ignored, so for example a rule with the source RHS "the JJ dog"
# would be allowed if the sequence "the slobbering dog" occurs in one of
# the input sentences, even if there's no rule to derive a JJ from
# "slobbering." (If "slobbering" were an unknown word, the 'unknown-lhs'
# decoder option would allow it to take a number of NT labels, likely
# including JJ, with varying probabilities, so removing the rule would
# be a bad idea.)
import optparse
import sys
class NGram(tuple):
pass
class Gap:
def __init__(self, minSpan):
self.minSpan = minSpan
def getMinSpan(self):
return self.minSpan
def printUsage():
sys.stderr.write(
"Usage: "
"filter-rule-table.py [--min-non-initial-rule-count=N] INPUT")
def main():
parser = optparse.OptionParser()
parser.add_option("-c", "--min-non-initial-rule-count",
action="store", dest="minCount",
type="float", default="0.0",
help="prune non-initial rules where count is below N",
metavar="N")
(options, args) = parser.parse_args()
if len(args) != 1:
printUsage()
sys.exit(1)
N = 7
inputSentences = []
for line in open(args[0]):
inputSentences.append(line.split())
filterRuleTable(sys.stdin, inputSentences, N, options)
def filterRuleTable(ruleTable, inputSentences, N, options):
# Map each input n-gram (n = 1..N) to a map from sentence indices to
# lists of intra-sentence indices.
occurrences = {}
for i, sentence in enumerate(inputSentences):
for n in range(1, N + 1):
for j in range(0, len(sentence) - n + 1):
ngram = NGram(sentence[j:j + n])
innerMap = occurrences.setdefault(ngram, {})
indices = innerMap.setdefault(i, [])
indices.append(j)
# Compare rules against input.
prevRHS = None
prevRuleIncluded = None
for line in ruleTable:
rhs, count = parseRule(line)
below_threshold = (count is not None and count < options.minCount)
# Prune non-initial rule if count is below threshold.
if below_threshold and isNonInitialRule(rhs):
if prevRHS != rhs:
prevRuleIncluded = None
prevRHS = rhs
continue
# If source RHS is same as last rule's then we already know whether to
# filter or not (unless it was pruned before checking).
if rhs == prevRHS and prevRuleIncluded is not None:
if prevRuleIncluded:
print line,
continue
prevRHS = rhs
# <s> and </s> can appear in glue rules.
if rhs[0] == "<s>" or rhs[-1] == "</s>":
print line,
prevRuleIncluded = True
continue
segments = segmentRHS(rhs, N)
ngramMaps = [
occurrences.get(s, {})
for s in segments
if isinstance(s, NGram)]
if len(ngramMaps) == 0:
print line,
prevRuleIncluded = True
continue
# Determine the sentences in which all n-grams co-occur.
sentences = set(ngramMaps[0].keys())
for ngramMap in ngramMaps[1:]:
sentences &= set(ngramMap.keys())
# Try to match rule in candidate sentences.
match = False
for sentenceIndex in sentences:
sentenceLength = len(inputSentences[sentenceIndex])
for indexSeq in enumerateIndexSeqs(ngramMaps, sentenceIndex, 0):
if matchSegments(segments, indexSeq, sentenceLength):
print line,
match = True
break
if match:
break
prevRuleIncluded = match
def parseRule(line):
"""Parse a line of the rule table.
:return: A tuple containing two items: the list of RHS source symbols,
and the rule count (if present).
"""
cols = line.split("|||")
rhsSourceSymbols = cols[0].split()[:-1]
ruleCount = None
if len(cols) > 4:
counts = cols[4].split()
if len(counts) == 3:
ruleCount = float(counts[2])
return (rhsSourceSymbols, ruleCount)
def isNT(symbol):
return symbol[0] == '[' and symbol[-1] == ']'
def isNonInitialRule(rhs):
for symbol in rhs:
if isNT(symbol):
return True
return False
def segmentRHS(rhs, N):
segments = []
terminals = []
minGapWidth = 0
for symbol in rhs:
if isNT(symbol):
if len(terminals) > 0:
assert minGapWidth == 0
segments.append(NGram(terminals))
terminals = []
minGapWidth += 1
else:
if minGapWidth > 0:
assert len(terminals) == 0
segments.append(Gap(minGapWidth))
minGapWidth = 0
terminals.append(symbol)
if len(terminals) == N:
segments.append(NGram(terminals))
terminals = []
if minGapWidth > 0:
assert len(terminals) == 0
segments.append(Gap(minGapWidth))
elif len(terminals) > 0:
segments.append(NGram(terminals))
return segments
def matchSegments(segments, indexSeq, sentenceLength):
assert len(segments) > 0
firstSegment = segments[0]
i = 0
if isinstance(firstSegment, Gap):
minPos = firstSegment.getMinSpan()
maxPos = sentenceLength - 1
else:
minPos = indexSeq[i] + len(firstSegment)
i += 1
maxPos = minPos
for segment in segments[1:]:
if isinstance(segment, Gap):
if minPos + segment.getMinSpan() > sentenceLength:
return False
minPos = minPos + segment.getMinSpan()
maxPos = sentenceLength - 1
else:
pos = indexSeq[i]
i += 1
if pos < minPos or pos > maxPos:
return False
minPos = pos + len(segment)
maxPos = minPos
return True
def enumerateIndexSeqs(ngramMaps, sentenceIndex, minFirstIndex):
assert len(ngramMaps) > 0
if len(ngramMaps) == 1:
for index in ngramMaps[0][sentenceIndex]:
if index >= minFirstIndex:
yield [index]
return
for index in ngramMaps[0][sentenceIndex]:
if index < minFirstIndex:
continue
for seq in enumerateIndexSeqs(ngramMaps[1:], sentenceIndex, index + 1):
assert seq[0] > index
yield [index] + seq
if __name__ == "__main__":
main()
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