mirror of https://github.com/explosion/spaCy.git
105 lines
3.6 KiB
Python
105 lines
3.6 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
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associate the words with the tags. We then search for tag-sequences that
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correspond to valid candidates. Finally, we look up the candidates in the hash
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set.
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For instance, to search for the phrases "Barack Hussein Obama" and "Hilary
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Clinton", we would associate "Barack" and "Hilary" with the B tag, Hussein with
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the I tag, 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
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candidate 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
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ever matching over the tag patterns. So no matter how many phrases we're
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looking for, our pattern set stays very small (exact size depends on the
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maximum length we're looking for, as the query language currently has no
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quantifiers).
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The example expects a .bz2 file from the Reddit corpus, and a patterns file,
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formatted in jsonl as a sequence of entries like this:
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{"text":"Anchorage"}
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{"text":"Angola"}
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{"text":"Ann Arbor"}
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{"text":"Annapolis"}
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{"text":"Appalachia"}
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{"text":"Argentina"}
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"""
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from __future__ import print_function, unicode_literals, division
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from bz2 import BZ2File
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import time
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import plac
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import ujson
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from spacy.matcher import PhraseMatcher
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import spacy
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@plac.annotations(
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patterns_loc=("Path to gazetteer", "positional", None, str),
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text_loc=("Path to Reddit corpus file", "positional", None, str),
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n=("Number of texts to read", "option", "n", int),
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lang=("Language class to initialise", "option", "l", str))
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def main(patterns_loc, text_loc, n=10000, lang='en'):
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nlp = spacy.blank('en')
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nlp.vocab.lex_attr_getters = {}
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phrases = read_gazetteer(nlp.tokenizer, patterns_loc)
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count = 0
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t1 = time.time()
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for ent_id, text in get_matches(nlp.tokenizer, phrases,
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read_text(text_loc, n=n)):
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count += 1
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t2 = time.time()
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print("%d docs in %.3f s. %d matches" % (n, (t2 - t1), count))
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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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data = ujson.loads(line.strip())
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phrase = tokenizer(data['text'])
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for w in phrase:
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_ = tokenizer.vocab[w.text]
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if len(phrase) >= 2:
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yield phrase
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def read_text(bz2_loc, n=10000):
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with BZ2File(bz2_loc) as file_:
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for i, line in enumerate(file_):
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data = ujson.loads(line)
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yield data['body']
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if i >= n:
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break
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def get_matches(tokenizer, phrases, texts, max_length=6):
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matcher = PhraseMatcher(tokenizer.vocab, max_length=max_length)
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matcher.add('Phrase', None, *phrases)
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for text in texts:
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doc = tokenizer(text)
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for w in doc:
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_ = doc.vocab[w.text]
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matches = matcher(doc)
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for ent_id, start, end in matches:
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yield (ent_id, doc[start:end].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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