Question

我知道如何使用NLTK获得bigram和trigram搭配，并将它们应用到我自己的语料库中。代码如下。

我不确定（1）如何获得特定单词的搭配？（2）NLTK是否具有基于对数似然比的搭配度量？

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

Answer 1

试试这段代码：

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“生物”一词的Ngrams

Answer 2

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东西。这种方法通常用于过滤掉ngram特定部分中的单词，你可以根据自己的内容进行调整。

Answer 3

关于问题＃2，是的！ NLTK在其关联度量中具有似然比。第一个问题仍然没有答案！

http://nltk.org/api/nltk.metrics.html?highlight=likelihood_ratio#nltk.metrics.association.NgramAssocMeasures.likelihood_ratio

针对特定单词的NLTK搭配

3 个答案: