如何防止在 NLTK 中拆分特定的单词或短语和数字？

Question

当我对拆分特定单词、日期和数字的文本进行标记时，我遇到了文本匹配问题。在 NLTK 中对单词进行标记时，如何防止诸如“run in my family”、“30 min walk”或“4x a day”之类的短语分裂？

它们不应导致：

['runs','in','my','family','4x','a','day']

例如：

是的，我每天骑 20-30 分钟，效果很好！！

给予：

['yes','20-30','minutes','a','day','on','my','bike',',','it','works','great']

我希望将“20-30 分钟”视为一个单词。我怎样才能得到这种行为>？

Answer 1

据我所知，您将很难在标记化的同时保留各种长度的 n-gram，但您可以找到这些 n-gram，如 here 所示。然后，您可以将语料库中您想要的 n-gram 项目替换为诸如破折号之类的连接字符。

这是一个示例解决方案，但可能有很多方法可以实现。 重要提示：我提供了一种查找文本中常见的 ngram 的方法（您可能需要超过 1 个，因此我在此处放置了一个变量，以便您决定要收集多少个 ngram . 你可能希望每种类型都有一个不同的数字，但我现在只给出了 1 个变量。）这可能会错过你认为重要的 ngram。为此，您可以将要查找的内容添加到user_grams。这些将被添加到搜索中。

import nltk 

#an example corpus
corpus='''A big tantrum runs in my family 4x a day, every week. 
A big tantrum is lame. A big tantrum causes strife. It runs in my family 
because of our complicated history. Every week is a lot though. Every week
I dread the tantrum. Every week...Here is another ngram I like a lot'''.lower()

#tokenize the corpus
corpus_tokens = nltk.word_tokenize(corpus)

#create ngrams from n=2 to 5
bigrams = list(nltk.ngrams(corpus_tokens,2))
trigrams = list(nltk.ngrams(corpus_tokens,3))
fourgrams = list(nltk.ngrams(corpus_tokens,4))
fivegrams = list(nltk.ngrams(corpus_tokens,5))

此部分查找常见的 ngram，最多为 5 个。

#if you change this to zero you will only get the user chosen ngrams
n_most_common=1 #how many of the most common n-grams do you want.

fdist_bigrams = nltk.FreqDist(bigrams).most_common(n_most_common) #n most common bigrams
fdist_trigrams = nltk.FreqDist(trigrams).most_common(n_most_common) #n most common trigrams
fdist_fourgrams = nltk.FreqDist(fourgrams).most_common(n_most_common) #n most common four grams
fdist_fivegrams = nltk.FreqDist(fivegrams).most_common(n_most_common) #n most common five grams

#concat the ngrams together
fdist_bigrams=[x[0][0]+' '+x[0][1] for x in fdist_bigrams]
fdist_trigrams=[x[0][0]+' '+x[0][1]+' '+x[0][2] for x in fdist_trigrams]
fdist_fourgrams=[x[0][0]+' '+x[0][1]+' '+x[0][2]+' '+x[0][3] for x in fdist_fourgrams]
fdist_fivegrams=[x[0][0]+' '+x[0][1]+' '+x[0][2]+' '+x[0][3]+' '+x[0][4]  for x in fdist_fivegrams]

#next 4 lines create a single list with important ngrams
n_grams=fdist_bigrams
n_grams.extend(fdist_trigrams)
n_grams.extend(fdist_fourgrams)
n_grams.extend(fdist_fivegrams)

此部分可让您将自己的 ngram 添加到列表中

#Another option here would be to make your own list of the ones you want
#in this example I add some user ngrams to the ones found above
user_grams=['ngram1 I like', 'ngram 2', 'another ngram I like a lot']
user_grams=[x.lower() for x in user_grams]    

n_grams.extend(user_grams)

最后一部分执行处理，以便您可以再次标记化并将 ngram 作为标记。

#initialize the corpus that will have combined ngrams
corpus_ngrams=corpus

#here we go through the ngrams we found and replace them in the corpus with
#version connected with dashes. That way we can find them when we tokenize.
for gram in n_grams:
    gram_r=gram.replace(' ','-')
    corpus_ngrams=corpus_ngrams.replace(gram, gram.replace(' ','-'))

#retokenize the new corpus so we can find the ngrams
corpus_ngrams_tokens= nltk.word_tokenize(corpus_ngrams)

print(corpus_ngrams_tokens)

Out: ['a-big-tantrum', 'runs-in-my-family', '4x', 'a', 'day', ',', 'every-week', '.', 'a-big-tantrum', 'is', 'lame', '.', 'a-big-tantrum', 'causes', 'strife', '.', 'it', 'runs-in-my-family', 'because', 'of', 'our', 'complicated', 'history', '.', 'every-week', 'is', 'a', 'lot', 'though', '.', 'every-week', 'i', 'dread', 'the', 'tantrum', '.', 'every-week', '...']

我认为这实际上是一个非常好的问题。

Answer 2

您可以使用MWETokenizer:

from nltk import word_tokenize
from nltk.tokenize import MWETokenizer

tokenizer = MWETokenizer([('20', '-', '30', 'minutes', 'a', 'day')])
tokenizer.tokenize(word_tokenize('Yes 20-30 minutes a day on my bike, it works great!!'))

[出]：

['Yes', '20-30_minutes_a_day', 'on', 'my', 'bike', ',', 'it', 'works', 'great', '!', '!']

---

一种更有原则的方法，因为您不知道 `word_tokenize 将如何拆分您要保留的单词：

from nltk import word_tokenize
from nltk.tokenize import MWETokenizer

def multiword_tokenize(text, mwe):
    # Initialize the MWETokenizer
    protected_tuples = [word_tokenize(word) for word in mwe]
    protected_tuples_underscore = ['_'.join(word) for word in protected_tuples]
    tokenizer = MWETokenizer(protected_tuples)
    # Tokenize the text.
    tokenized_text = tokenizer.tokenize(word_tokenize(text))
    # Replace the underscored protected words with the original MWE
    for i, token in enumerate(tokenized_text):
        if token in protected_tuples_underscore:
            tokenized_text[i] = mwe[protected_tuples_underscore.index(token)]
    return tokenized_text

mwe = ['20-30 minutes a day', '!!']
print(multiword_tokenize('Yes 20-30 minutes a day on my bike, it works great!!', mwe))

[出]：

['Yes', '20-30 minutes a day', 'on', 'my', 'bike', ',', 'it', 'works', 'great', '!!']