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99 lines
3.5 KiB
Python
99 lines
3.5 KiB
Python
"""Match a large set of multi-word expressions in O(1) time.
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The idea is to associate each word in the vocabulary with a tag, noting whether
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they begin, end, or are inside at least one pattern. An additional tag is used
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for single-word patterns. Complete patterns are also stored in a hash set.
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When we process a document, we look up the words in the vocabulary, to associate
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the words with the tags. We then search for tag-sequences that correspond to
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valid candidates. Finally, we look up the candidates in the hash set.
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For instance, to search for the phrases "Barack Hussein Obama" and "Hilary Clinton", we
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would associate "Barack" and "Hilary" with the B tag, Hussein with the I tag,
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and Obama and Clinton with the L tag.
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The document "Barack Clinton and Hilary Clinton" would have the tag sequence
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[{B}, {L}, {}, {B}, {L}], so we'd get two matches. However, only the second candidate
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is in the phrase dictionary, so only one is returned as a match.
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The algorithm is O(n) at run-time for document of length n because we're only ever
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matching over the tag patterns. So no matter how many phrases we're looking for,
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our pattern set stays very small (exact size depends on the maximum length we're
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looking for, as the query language currently has no quantifiers)
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"""
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from __future__ import print_function, unicode_literals, division
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from ast import literal_eval
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from bz2 import BZ2File
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import time
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import math
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import codecs
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import plac
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from preshed.maps import PreshMap
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from preshed.counter import PreshCounter
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from spacy.strings import hash_string
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from spacy.en import English
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from spacy.matcher import PhraseMatcher
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def read_gazetteer(tokenizer, loc, n=-1):
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for i, line in enumerate(open(loc)):
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phrase = literal_eval('u' + line.strip())
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if ' (' in phrase and phrase.endswith(')'):
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phrase = phrase.split(' (', 1)[0]
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if i >= n:
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break
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phrase = tokenizer(phrase)
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if all((t.is_lower and t.prob >= -10) for t in phrase):
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continue
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if len(phrase) >= 2:
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yield phrase
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def read_text(bz2_loc):
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with BZ2File(bz2_loc) as file_:
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for line in file_:
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yield line.decode('utf8')
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def get_matches(tokenizer, phrases, texts, max_length=6):
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matcher = PhraseMatcher(tokenizer.vocab, phrases, max_length=max_length)
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print("Match")
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for text in texts:
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doc = tokenizer(text)
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matches = matcher(doc)
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for mwe in doc.ents:
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yield mwe
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def main(patterns_loc, text_loc, counts_loc, n=10000000):
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nlp = English(parser=False, tagger=False, entity=False)
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print("Make matcher")
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phrases = read_gazetteer(nlp.tokenizer, patterns_loc, n=n)
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counts = PreshCounter()
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t1 = time.time()
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for mwe in get_matches(nlp.tokenizer, phrases, read_text(text_loc)):
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counts.inc(hash_string(mwe.text), 1)
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t2 = time.time()
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print("10m tokens in %d s" % (t2 - t1))
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with codecs.open(counts_loc, 'w', 'utf8') as file_:
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for phrase in read_gazetteer(nlp.tokenizer, patterns_loc, n=n):
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text = phrase.string
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key = hash_string(text)
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count = counts[key]
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if count != 0:
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file_.write('%d\t%s\n' % (count, text))
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if __name__ == '__main__':
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if False:
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import cProfile
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import pstats
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cProfile.runctx("plac.call(main)", globals(), locals(), "Profile.prof")
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s = pstats.Stats("Profile.prof")
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s.strip_dirs().sort_stats("time").print_stats()
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else:
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plac.call(main)
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