409 lines
14 KiB
Python
409 lines
14 KiB
Python
# Natural Language Toolkit: Collocations and Association Measures
|
|
#
|
|
# Copyright (C) 2001-2019 NLTK Project
|
|
# Author: Joel Nothman <jnothman@student.usyd.edu.au>
|
|
# URL: <http://nltk.org>
|
|
# For license information, see LICENSE.TXT
|
|
#
|
|
"""
|
|
Tools to identify collocations --- words that often appear consecutively
|
|
--- within corpora. They may also be used to find other associations between
|
|
word occurrences.
|
|
See Manning and Schutze ch. 5 at http://nlp.stanford.edu/fsnlp/promo/colloc.pdf
|
|
and the Text::NSP Perl package at http://ngram.sourceforge.net
|
|
|
|
Finding collocations requires first calculating the frequencies of words and
|
|
their appearance in the context of other words. Often the collection of words
|
|
will then requiring filtering to only retain useful content terms. Each ngram
|
|
of words may then be scored according to some association measure, in order
|
|
to determine the relative likelihood of each ngram being a collocation.
|
|
|
|
The ``BigramCollocationFinder`` and ``TrigramCollocationFinder`` classes provide
|
|
these functionalities, dependent on being provided a function which scores a
|
|
ngram given appropriate frequency counts. A number of standard association
|
|
measures are provided in bigram_measures and trigram_measures.
|
|
"""
|
|
from __future__ import print_function
|
|
|
|
# Possible TODOs:
|
|
# - consider the distinction between f(x,_) and f(x) and whether our
|
|
# approximation is good enough for fragmented data, and mention it
|
|
# - add a n-gram collocation finder with measures which only utilise n-gram
|
|
# and unigram counts (raw_freq, pmi, student_t)
|
|
|
|
import itertools as _itertools
|
|
from six import iteritems
|
|
|
|
from nltk.probability import FreqDist
|
|
from nltk.util import ngrams
|
|
# these two unused imports are referenced in collocations.doctest
|
|
from nltk.metrics import ContingencyMeasures, BigramAssocMeasures, TrigramAssocMeasures, QuadgramAssocMeasures
|
|
from nltk.metrics.spearman import ranks_from_scores, spearman_correlation
|
|
|
|
|
|
class AbstractCollocationFinder(object):
|
|
"""
|
|
An abstract base class for collocation finders whose purpose is to
|
|
collect collocation candidate frequencies, filter and rank them.
|
|
|
|
As a minimum, collocation finders require the frequencies of each
|
|
word in a corpus, and the joint frequency of word tuples. This data
|
|
should be provided through nltk.probability.FreqDist objects or an
|
|
identical interface.
|
|
"""
|
|
|
|
def __init__(self, word_fd, ngram_fd):
|
|
self.word_fd = word_fd
|
|
self.N = word_fd.N()
|
|
self.ngram_fd = ngram_fd
|
|
|
|
@classmethod
|
|
def _build_new_documents(
|
|
cls, documents, window_size, pad_left=False, pad_right=False, pad_symbol=None
|
|
):
|
|
'''
|
|
Pad the document with the place holder according to the window_size
|
|
'''
|
|
padding = (pad_symbol,) * (window_size - 1)
|
|
if pad_right:
|
|
return _itertools.chain.from_iterable(
|
|
_itertools.chain(doc, padding) for doc in documents
|
|
)
|
|
if pad_left:
|
|
return _itertools.chain.from_iterable(
|
|
_itertools.chain(padding, doc) for doc in documents
|
|
)
|
|
|
|
@classmethod
|
|
def from_documents(cls, documents):
|
|
"""Constructs a collocation finder given a collection of documents,
|
|
each of which is a list (or iterable) of tokens.
|
|
"""
|
|
# return cls.from_words(_itertools.chain(*documents))
|
|
return cls.from_words(
|
|
cls._build_new_documents(documents, cls.default_ws, pad_right=True)
|
|
)
|
|
|
|
@staticmethod
|
|
def _ngram_freqdist(words, n):
|
|
return FreqDist(tuple(words[i : i + n]) for i in range(len(words) - 1))
|
|
|
|
def _apply_filter(self, fn=lambda ngram, freq: False):
|
|
"""Generic filter removes ngrams from the frequency distribution
|
|
if the function returns True when passed an ngram tuple.
|
|
"""
|
|
tmp_ngram = FreqDist()
|
|
for ngram, freq in iteritems(self.ngram_fd):
|
|
if not fn(ngram, freq):
|
|
tmp_ngram[ngram] = freq
|
|
self.ngram_fd = tmp_ngram
|
|
|
|
def apply_freq_filter(self, min_freq):
|
|
"""Removes candidate ngrams which have frequency less than min_freq."""
|
|
self._apply_filter(lambda ng, freq: freq < min_freq)
|
|
|
|
def apply_ngram_filter(self, fn):
|
|
"""Removes candidate ngrams (w1, w2, ...) where fn(w1, w2, ...)
|
|
evaluates to True.
|
|
"""
|
|
self._apply_filter(lambda ng, f: fn(*ng))
|
|
|
|
def apply_word_filter(self, fn):
|
|
"""Removes candidate ngrams (w1, w2, ...) where any of (fn(w1), fn(w2),
|
|
...) evaluates to True.
|
|
"""
|
|
self._apply_filter(lambda ng, f: any(fn(w) for w in ng))
|
|
|
|
def _score_ngrams(self, score_fn):
|
|
"""Generates of (ngram, score) pairs as determined by the scoring
|
|
function provided.
|
|
"""
|
|
for tup in self.ngram_fd:
|
|
score = self.score_ngram(score_fn, *tup)
|
|
if score is not None:
|
|
yield tup, score
|
|
|
|
def score_ngrams(self, score_fn):
|
|
"""Returns a sequence of (ngram, score) pairs ordered from highest to
|
|
lowest score, as determined by the scoring function provided.
|
|
"""
|
|
return sorted(self._score_ngrams(score_fn), key=lambda t: (-t[1], t[0]))
|
|
|
|
def nbest(self, score_fn, n):
|
|
"""Returns the top n ngrams when scored by the given function."""
|
|
return [p for p, s in self.score_ngrams(score_fn)[:n]]
|
|
|
|
def above_score(self, score_fn, min_score):
|
|
"""Returns a sequence of ngrams, ordered by decreasing score, whose
|
|
scores each exceed the given minimum score.
|
|
"""
|
|
for ngram, score in self.score_ngrams(score_fn):
|
|
if score > min_score:
|
|
yield ngram
|
|
else:
|
|
break
|
|
|
|
|
|
class BigramCollocationFinder(AbstractCollocationFinder):
|
|
"""A tool for the finding and ranking of bigram collocations or other
|
|
association measures. It is often useful to use from_words() rather than
|
|
constructing an instance directly.
|
|
"""
|
|
|
|
default_ws = 2
|
|
|
|
def __init__(self, word_fd, bigram_fd, window_size=2):
|
|
"""Construct a BigramCollocationFinder, given FreqDists for
|
|
appearances of words and (possibly non-contiguous) bigrams.
|
|
"""
|
|
AbstractCollocationFinder.__init__(self, word_fd, bigram_fd)
|
|
self.window_size = window_size
|
|
|
|
@classmethod
|
|
def from_words(cls, words, window_size=2):
|
|
"""Construct a BigramCollocationFinder for all bigrams in the given
|
|
sequence. When window_size > 2, count non-contiguous bigrams, in the
|
|
style of Church and Hanks's (1990) association ratio.
|
|
"""
|
|
wfd = FreqDist()
|
|
bfd = FreqDist()
|
|
|
|
if window_size < 2:
|
|
raise ValueError("Specify window_size at least 2")
|
|
|
|
for window in ngrams(words, window_size, pad_right=True):
|
|
w1 = window[0]
|
|
if w1 is None:
|
|
continue
|
|
wfd[w1] += 1
|
|
for w2 in window[1:]:
|
|
if w2 is not None:
|
|
bfd[(w1, w2)] += 1
|
|
return cls(wfd, bfd, window_size=window_size)
|
|
|
|
def score_ngram(self, score_fn, w1, w2):
|
|
"""Returns the score for a given bigram using the given scoring
|
|
function. Following Church and Hanks (1990), counts are scaled by
|
|
a factor of 1/(window_size - 1).
|
|
"""
|
|
n_all = self.N
|
|
n_ii = self.ngram_fd[(w1, w2)] / (self.window_size - 1.0)
|
|
if not n_ii:
|
|
return
|
|
n_ix = self.word_fd[w1]
|
|
n_xi = self.word_fd[w2]
|
|
return score_fn(n_ii, (n_ix, n_xi), n_all)
|
|
|
|
|
|
class TrigramCollocationFinder(AbstractCollocationFinder):
|
|
"""A tool for the finding and ranking of trigram collocations or other
|
|
association measures. It is often useful to use from_words() rather than
|
|
constructing an instance directly.
|
|
"""
|
|
|
|
default_ws = 3
|
|
|
|
def __init__(self, word_fd, bigram_fd, wildcard_fd, trigram_fd):
|
|
"""Construct a TrigramCollocationFinder, given FreqDists for
|
|
appearances of words, bigrams, two words with any word between them,
|
|
and trigrams.
|
|
"""
|
|
AbstractCollocationFinder.__init__(self, word_fd, trigram_fd)
|
|
self.wildcard_fd = wildcard_fd
|
|
self.bigram_fd = bigram_fd
|
|
|
|
@classmethod
|
|
def from_words(cls, words, window_size=3):
|
|
"""Construct a TrigramCollocationFinder for all trigrams in the given
|
|
sequence.
|
|
"""
|
|
if window_size < 3:
|
|
raise ValueError("Specify window_size at least 3")
|
|
|
|
wfd = FreqDist()
|
|
wildfd = FreqDist()
|
|
bfd = FreqDist()
|
|
tfd = FreqDist()
|
|
for window in ngrams(words, window_size, pad_right=True):
|
|
w1 = window[0]
|
|
if w1 is None:
|
|
continue
|
|
for w2, w3 in _itertools.combinations(window[1:], 2):
|
|
wfd[w1] += 1
|
|
if w2 is None:
|
|
continue
|
|
bfd[(w1, w2)] += 1
|
|
if w3 is None:
|
|
continue
|
|
wildfd[(w1, w3)] += 1
|
|
tfd[(w1, w2, w3)] += 1
|
|
return cls(wfd, bfd, wildfd, tfd)
|
|
|
|
def bigram_finder(self):
|
|
"""Constructs a bigram collocation finder with the bigram and unigram
|
|
data from this finder. Note that this does not include any filtering
|
|
applied to this finder.
|
|
"""
|
|
return BigramCollocationFinder(self.word_fd, self.bigram_fd)
|
|
|
|
def score_ngram(self, score_fn, w1, w2, w3):
|
|
"""Returns the score for a given trigram using the given scoring
|
|
function.
|
|
"""
|
|
n_all = self.N
|
|
n_iii = self.ngram_fd[(w1, w2, w3)]
|
|
if not n_iii:
|
|
return
|
|
n_iix = self.bigram_fd[(w1, w2)]
|
|
n_ixi = self.wildcard_fd[(w1, w3)]
|
|
n_xii = self.bigram_fd[(w2, w3)]
|
|
n_ixx = self.word_fd[w1]
|
|
n_xix = self.word_fd[w2]
|
|
n_xxi = self.word_fd[w3]
|
|
return score_fn(n_iii, (n_iix, n_ixi, n_xii), (n_ixx, n_xix, n_xxi), n_all)
|
|
|
|
|
|
class QuadgramCollocationFinder(AbstractCollocationFinder):
|
|
"""A tool for the finding and ranking of quadgram collocations or other association measures.
|
|
It is often useful to use from_words() rather than constructing an instance directly.
|
|
"""
|
|
|
|
default_ws = 4
|
|
|
|
def __init__(self, word_fd, quadgram_fd, ii, iii, ixi, ixxi, iixi, ixii):
|
|
"""Construct a QuadgramCollocationFinder, given FreqDists for appearances of words,
|
|
bigrams, trigrams, two words with one word and two words between them, three words
|
|
with a word between them in both variations.
|
|
"""
|
|
AbstractCollocationFinder.__init__(self, word_fd, quadgram_fd)
|
|
self.iii = iii
|
|
self.ii = ii
|
|
self.ixi = ixi
|
|
self.ixxi = ixxi
|
|
self.iixi = iixi
|
|
self.ixii = ixii
|
|
|
|
@classmethod
|
|
def from_words(cls, words, window_size=4):
|
|
if window_size < 4:
|
|
raise ValueError("Specify window_size at least 4")
|
|
ixxx = FreqDist()
|
|
iiii = FreqDist()
|
|
ii = FreqDist()
|
|
iii = FreqDist()
|
|
ixi = FreqDist()
|
|
ixxi = FreqDist()
|
|
iixi = FreqDist()
|
|
ixii = FreqDist()
|
|
|
|
for window in ngrams(words, window_size, pad_right=True):
|
|
w1 = window[0]
|
|
if w1 is None:
|
|
continue
|
|
for w2, w3, w4 in _itertools.combinations(window[1:], 3):
|
|
ixxx[w1] += 1
|
|
if w2 is None:
|
|
continue
|
|
ii[(w1, w2)] += 1
|
|
if w3 is None:
|
|
continue
|
|
iii[(w1, w2, w3)] += 1
|
|
ixi[(w1, w3)] += 1
|
|
if w4 is None:
|
|
continue
|
|
iiii[(w1, w2, w3, w4)] += 1
|
|
ixxi[(w1, w4)] += 1
|
|
ixii[(w1, w3, w4)] += 1
|
|
iixi[(w1, w2, w4)] += 1
|
|
|
|
return cls(ixxx, iiii, ii, iii, ixi, ixxi, iixi, ixii)
|
|
|
|
def score_ngram(self, score_fn, w1, w2, w3, w4):
|
|
n_all = self.N
|
|
n_iiii = self.ngram_fd[(w1, w2, w3, w4)]
|
|
if not n_iiii:
|
|
return
|
|
n_iiix = self.iii[(w1, w2, w3)]
|
|
n_xiii = self.iii[(w2, w3, w4)]
|
|
n_iixi = self.iixi[(w1, w2, w4)]
|
|
n_ixii = self.ixii[(w1, w3, w4)]
|
|
|
|
n_iixx = self.ii[(w1, w2)]
|
|
n_xxii = self.ii[(w3, w4)]
|
|
n_xiix = self.ii[(w2, w3)]
|
|
n_ixix = self.ixi[(w1, w3)]
|
|
n_ixxi = self.ixxi[(w1, w4)]
|
|
n_xixi = self.ixi[(w2, w4)]
|
|
|
|
n_ixxx = self.word_fd[w1]
|
|
n_xixx = self.word_fd[w2]
|
|
n_xxix = self.word_fd[w3]
|
|
n_xxxi = self.word_fd[w4]
|
|
return score_fn(
|
|
n_iiii,
|
|
(n_iiix, n_iixi, n_ixii, n_xiii),
|
|
(n_iixx, n_ixix, n_ixxi, n_xixi, n_xxii, n_xiix),
|
|
(n_ixxx, n_xixx, n_xxix, n_xxxi),
|
|
n_all,
|
|
)
|
|
|
|
|
|
def demo(scorer=None, compare_scorer=None):
|
|
"""Finds bigram collocations in the files of the WebText corpus."""
|
|
from nltk.metrics import (
|
|
BigramAssocMeasures,
|
|
spearman_correlation,
|
|
ranks_from_scores,
|
|
)
|
|
|
|
if scorer is None:
|
|
scorer = BigramAssocMeasures.likelihood_ratio
|
|
if compare_scorer is None:
|
|
compare_scorer = BigramAssocMeasures.raw_freq
|
|
|
|
from nltk.corpus import stopwords, webtext
|
|
|
|
ignored_words = stopwords.words('english')
|
|
word_filter = lambda w: len(w) < 3 or w.lower() in ignored_words
|
|
|
|
for file in webtext.fileids():
|
|
words = [word.lower() for word in webtext.words(file)]
|
|
|
|
cf = BigramCollocationFinder.from_words(words)
|
|
cf.apply_freq_filter(3)
|
|
cf.apply_word_filter(word_filter)
|
|
|
|
corr = spearman_correlation(
|
|
ranks_from_scores(cf.score_ngrams(scorer)),
|
|
ranks_from_scores(cf.score_ngrams(compare_scorer)),
|
|
)
|
|
print(file)
|
|
print('\t', [' '.join(tup) for tup in cf.nbest(scorer, 15)])
|
|
print('\t Correlation to %s: %0.4f' % (compare_scorer.__name__, corr))
|
|
|
|
|
|
# Slows down loading too much
|
|
# bigram_measures = BigramAssocMeasures()
|
|
# trigram_measures = TrigramAssocMeasures()
|
|
|
|
if __name__ == '__main__':
|
|
import sys
|
|
from nltk.metrics import BigramAssocMeasures
|
|
|
|
try:
|
|
scorer = eval('BigramAssocMeasures.' + sys.argv[1])
|
|
except IndexError:
|
|
scorer = None
|
|
try:
|
|
compare_scorer = eval('BigramAssocMeasures.' + sys.argv[2])
|
|
except IndexError:
|
|
compare_scorer = None
|
|
|
|
demo(scorer, compare_scorer)
|
|
|
|
__all__ = [
|
|
'BigramCollocationFinder',
|
|
'TrigramCollocationFinder',
|
|
'QuadgramCollocationFinder',
|
|
]
|