Question

我正在尝试document classification, as described in NLTK Chapter 6，我在删除停用词时遇到问题。当我添加

all_words = (w for w in all_words if w not in nltk.corpus.stopwords.words('english'))

它返回

Traceback (most recent call last):
  File "fiction.py", line 8, in <module>
    word_features = all_words.keys()[:100]
AttributeError: 'generator' object has no attribute 'keys'

我猜测停用词代码改变了用于'all_words'的对象类型，使得它们.key（）函数无用。如何在使用键功能之前删除停用词而不更改其类型？完整代码如下：

import nltk 
from nltk.corpus import PlaintextCorpusReader

corpus_root = './nltk_data/corpora/fiction'
fiction = PlaintextCorpusReader(corpus_root, '.*')
all_words=nltk.FreqDist(w.lower() for w in fiction.words())
all_words = (w for w in all_words if w not in nltk.corpus.stopwords.words('english'))
word_features = all_words.keys()[:100]

def document_features(document): # [_document-classify-extractor]
    document_words = set(document) # [_document-classify-set]
    features = {}
    for word in word_features:
        features['contains(%s)' % word] = (word in document_words)
    return features

print document_features(fiction.words('fic/11.txt'))

Answer 1

我会通过避免首先将它们添加到FreqDist实例来实现这一点：

all_words=nltk.FreqDist(w.lower() for w in fiction.words() if w.lower() not in nltk.corpus.stopwords.words('english'))

根据你的语料库的大小，我认为你可能会在创建一个停用词集之前获得性能提升：

stopword_set = frozenset(ntlk.corpus.stopwords.words('english'))

如果这不适合您的情况，看起来您可以利用FreqDist继承自dict的事实：

for stopword in nltk.corpus.stopwords.words('english'):
    if stopword in all_words:
        del all_words[stopword]

NLTK禁用词删除问题

1 个答案: