如何获得句子文本中二元组的概率？

Question

我有一个包含很多句子的文本。我如何使用nltk.ngrams它来处理它？

这是我的代码：

   sequence = nltk.tokenize.word_tokenize(raw) 
   bigram = ngrams(sequence,2)
   freq_dist = nltk.FreqDist(bigram)
   prob_dist = nltk.MLEProbDist(freq_dist)
   number_of_bigrams = freq_dist.N()

但是，上面的代码假设所有句子都是一个序列。但是，句子是分开的，我猜一个句子的最后一个词与另一个句子的起始词无关。如何bigram为这样的文本创建一个？我还需要prob_dist并且number_of_bigrams基于`freq_dist。

有类似这样的问题什么是 ngram 计数以及如何使用 nltk 实现？但它们主要是关于一个单词序列。

Answer 1

您可以使用新nltk.lm模块。这是一个示例，首先获取一些数据并将其标记化：

import os
import requests
import io #codecs

from nltk import word_tokenize, sent_tokenize 

# Text version of https://kilgarriff.co.uk/Publications/2005-K-lineer.pdf
if os.path.isfile('language-never-random.txt'):
    with io.open('language-never-random.txt', encoding='utf8') as fin:
        text = fin.read()
else:
    url = "https://gist.githubusercontent.com/alvations/53b01e4076573fea47c6057120bb017a/raw/b01ff96a5f76848450e648f35da6497ca9454e4a/language-never-random.txt"
    text = requests.get(url).content.decode('utf8')
    with io.open('language-never-random.txt', 'w', encoding='utf8') as fout:
        fout.write(text)

# Tokenize the text.
tokenized_text = [list(map(str.lower, word_tokenize(sent))) 
              for sent in sent_tokenize(text)]

然后是语言建模：

# Preprocess the tokenized text for 3-grams language modelling
from nltk.lm.preprocessing import padded_everygram_pipeline
from nltk.lm import MLE

n = 3
train_data, padded_sents = padded_everygram_pipeline(n, tokenized_text)

model = MLE(n) # Lets train a 3-grams maximum likelihood estimation model.
model.fit(train_data, padded_sents)

要获得计数：

model.counts['language'] # i.e. Count('language')
model.counts[['language']]['is'] # i.e. Count('is'|'language')
model.counts[['language', 'is']]['never'] # i.e. Count('never'|'language is')

获取概率：

model.score('is', 'language'.split())  # P('is'|'language')
model.score('never', 'language is'.split())  # P('never'|'language is')

加载笔记本时 Kaggle 平台上有一些问题，但在某些时候，这个笔记本应该很好地概述了nltk.lm模块https://www.kaggle.com/alvations/n-gram-language-model-with-nltk