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.
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.
