mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-25 17:36:30 +03:00
99 lines
3.3 KiB
Python
99 lines
3.3 KiB
Python
"""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 set.
|
|
|
|
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 quantifiers)
|
|
|
|
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":"Anchorage"}
|
|
{"text":"Angola"}
|
|
{"text":"Ann Arbor"}
|
|
{"text":"Annapolis"}
|
|
{"text":"Appalachia"}
|
|
{"text":"Argentina"}
|
|
"""
|
|
from __future__ import print_function, unicode_literals, division
|
|
from bz2 import BZ2File
|
|
import time
|
|
import math
|
|
import codecs
|
|
|
|
import plac
|
|
import ujson
|
|
|
|
from spacy.matcher import PhraseMatcher
|
|
import spacy
|
|
|
|
|
|
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:
|
|
break
|
|
|
|
|
|
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)
|
|
|
|
|
|
def main(patterns_loc, text_loc, n=10000):
|
|
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))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
if False:
|
|
import cProfile
|
|
import pstats
|
|
cProfile.runctx("plac.call(main)", globals(), locals(), "Profile.prof")
|
|
s = pstats.Stats("Profile.prof")
|
|
s.strip_dirs().sort_stats("time").print_stats()
|
|
else:
|
|
plac.call(main)
|