python-如何获取句子文本中的双峰概率？

我的文本中有很多句子.如何使用nltk.ngrams进行处理？

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,以上代码假定所有句子都是一个序列.但是,句子是分开的,我想一个句子的最后一个词与另一个句子的开始词无关.如何为这样的文本创建一个双字母组？我还需要基于`freq_dist的prob_dist和number_of_bigrams.

也有类似What are ngram counts and how to implement using nltk?的类似问题,但它们大多与单词序列有关.

最佳答案

您可以使用新的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)]

然后进行语言建模：

# 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)

获取计数：

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,但在某些情况下,该笔记本应该可以很好地概述nltk.lm模块https://www.kaggle.com/alvations/n-gram-language-model-with-nltk.

n-gram

python-如何获取句子文本中的双峰概率？

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