3f2e3cb May 31, 2018
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#!/usr/bin/env python
# coding: utf8
"""Match a large set of multi-word expressions in O(1) time.
The idea is to associate each word in the vocabulary with a tag, noting whether
they begin, end, or are inside at least one pattern. An additional tag is used
for single-word patterns. Complete patterns are also stored in a hash set.
When we process a document, we look up the words in the vocabulary, to
associate the words with the tags. We then search for tag-sequences that
correspond to valid candidates. Finally, we look up the candidates in the hash
For instance, to search for the phrases "Barack Hussein Obama" and "Hilary
Clinton", we would associate "Barack" and "Hilary" with the B tag, Hussein with
the I tag, and Obama and Clinton with the L tag.
The document "Barack Clinton and Hilary Clinton" would have the tag sequence
[{B}, {L}, {}, {B}, {L}], so we'd get two matches. However, only the second
candidate is in the phrase dictionary, so only one is returned as a match.
The algorithm is O(n) at run-time for document of length n because we're only
ever matching over the tag patterns. So no matter how many phrases we're
looking for, our pattern set stays very small (exact size depends on the
maximum length we're looking for, as the query language currently has no
The example expects a .bz2 file from the Reddit corpus, and a patterns file,
formatted in jsonl as a sequence of entries like this:
{"text":"Ann Arbor"}
Reddit comments corpus:
Compatible with: spaCy v2.0.0+
from __future__ import print_function, unicode_literals, division
from bz2 import BZ2File
import time
import plac
import ujson
from spacy.matcher import PhraseMatcher
import spacy
patterns_loc=("Path to gazetteer", "positional", None, str),
text_loc=("Path to Reddit corpus file", "positional", None, str),
n=("Number of texts to read", "option", "n", int),
lang=("Language class to initialise", "option", "l", str))
def main(patterns_loc, text_loc, n=10000, lang='en'):
nlp = spacy.blank('en')
nlp.vocab.lex_attr_getters = {}
phrases = read_gazetteer(nlp.tokenizer, patterns_loc)
count = 0
t1 = time.time()
for ent_id, text in get_matches(nlp.tokenizer, phrases,
read_text(text_loc, n=n)):
count += 1
t2 = time.time()
print("%d docs in %.3f s. %d matches" % (n, (t2 - t1), count))
def read_gazetteer(tokenizer, loc, n=-1):
for i, line in enumerate(open(loc)):
data = ujson.loads(line.strip())
phrase = tokenizer(data['text'])
for w in phrase:
_ = tokenizer.vocab[w.text]
if len(phrase) >= 2:
yield phrase
def read_text(bz2_loc, n=10000):
with BZ2File(bz2_loc) as file_:
for i, line in enumerate(file_):
data = ujson.loads(line)
yield data['body']
if i >= n:
def get_matches(tokenizer, phrases, texts, max_length=6):
matcher = PhraseMatcher(tokenizer.vocab, max_length=max_length)
matcher.add('Phrase', None, *phrases)
for text in texts:
doc = tokenizer(text)
for w in doc:
_ = doc.vocab[w.text]
matches = matcher(doc)
for ent_id, start, end in matches:
yield (ent_id, doc[start:end].text)
if __name__ == '__main__':
if False:
import cProfile
import pstats
cProfile.runctx("", globals(), locals(), "")
s = pstats.Stats("")