如何在句子文本中获得bigrams的概率？

Question

我有一个有很多句子的文字。我怎样才能使用nltk.ngrams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单词与另一个句子的起始单词无关。如何为这样的文本创建bigram？我还需要基于`freq_dist的prob_dist和number_of_bigrams。

有像What are ngram counts and how to implement using nltk?这样的类似问题，但它们主要是关于一系列单词。

Answer 1

您可以使用新的nltk.lm模块。这是一个例子，首先获取一些数据并对其进行标记：

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)

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')

获得概率：

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

---

装载笔记本电脑时，Kaggle平台上有一些问题，但在某些时候，这款笔记本应该能够很好地概述nltk.lm模块https://www.kaggle.com/alvations/n-gram-language-model-with-nltk