我知道如何使用 NLTK 获得二元组和三元组搭配，并将它们应用到我自己的语料库中。代码如下。
但是我不确定（1）如何获取特定单词的搭配？(2) NLTK 是否有基于对数似然比的搭配指标？
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
试试这个代码：
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)
它使用似然度量并过滤掉不包含单词“creature”的 Ngram
问题 1 - 尝试：
target_word = "electronic" # your choice of word
finder.apply_ngram_filter(lambda w1, w2, w3: target_word not in (w1, w2, w3))
for i in finder.score_ngrams(trigram_measures.likelihood_ratio):
print i
这个想法是过滤掉你不想要的任何东西。此方法通常用于过滤掉 ngram 特定部分中的单词，您可以根据自己的喜好对其进行调整。
至于问题#2，是的！NLTK 在其关联度量中具有似然比。第一个问题没有答案！