I know how to get bigram and trigram collocations using NLTK and I apply them to my own corpora. The code is below.
I'm not sure however about (1) how to get the collocations for a particular word? (2) does NLTK have a collocation metric based on Log-Likelihood Ratio?
import nltk
from nltk.collocations import *
from nltk.tokenize import word_tokenize
text = "this is a foo bar bar black sheep foo bar bar black sheep foo bar bar black sheep shep bar bar black sentence"
trigram_measures = nltk.collocations.TrigramAssocMeasures()
finder = TrigramCollocationFinder.from_words(word_tokenize(text))
for i in finder.score_ngrams(trigram_measures.pmi):
print i
Try this code:
import nltk
from nltk.collocations import *
bigram_measures = nltk.collocations.BigramAssocMeasures()
trigram_measures = nltk.collocations.TrigramAssocMeasures()
# Ngrams with 'creature' as a member
creature_filter = lambda *w: 'creature' not in w
## Bigrams
finder = BigramCollocationFinder.from_words(
nltk.corpus.genesis.words('english-web.txt'))
# only bigrams that appear 3+ times
finder.apply_freq_filter(3)
# only bigrams that contain 'creature'
finder.apply_ngram_filter(creature_filter)
# return the 10 n-grams with the highest PMI
print finder.nbest(bigram_measures.likelihood_ratio, 10)
## Trigrams
finder = TrigramCollocationFinder.from_words(
nltk.corpus.genesis.words('english-web.txt'))
# only trigrams that appear 3+ times
finder.apply_freq_filter(3)
# only trigrams that contain 'creature'
finder.apply_ngram_filter(creature_filter)
# return the 10 n-grams with the highest PMI
print finder.nbest(trigram_measures.likelihood_ratio, 10)
It uses the likelihood measure and also filters out Ngrams that don't contain the word 'creature'