我正在使用 NLTK 和 scikit-learn 的 CountVectorizer 的组合来进行词干提取和标记化。
以下是 CountVectorizer 的简单用法示例:
from sklearn.feature_extraction.text import CountVectorizer
vocab = ['The swimmer likes swimming so he swims.']
vec = CountVectorizer().fit(vocab)
sentence1 = vec.transform(['The swimmer likes swimming.'])
sentence2 = vec.transform(['The swimmer swims.'])
print('Vocabulary: %s' %vec.get_feature_names())
print('Sentence 1: %s' %sentence1.toarray())
print('Sentence 2: %s' %sentence2.toarray())
哪个会打印
Vocabulary: ['he', 'likes', 'so', 'swimmer', 'swimming', 'swims', 'the']
Sentence 1: [[0 1 0 1 1 0 1]]
Sentence 2: [[0 0 0 1 0 1 1]]
现在，假设我要删除停用词并阻止这些词。一种选择是这样做:
from nltk import word_tokenize
from nltk.stem.porter import PorterStemmer
#######
# based on http://www.cs.duke.edu/courses/spring14/compsci290/assignments/lab02.html
stemmer = PorterStemmer()
def stem_tokens(tokens, stemmer):
stemmed = []
for item in tokens:
stemmed.append(stemmer.stem(item))
return stemmed
def tokenize(text):
tokens = nltk.word_tokenize(text)
stems = stem_tokens(tokens, stemmer)
return stems
########
vect = CountVectorizer(tokenizer=tokenize, stop_words='english')
vect.fit(vocab)
sentence1 = vect.transform(['The swimmer likes swimming.'])
sentence2 = vect.transform(['The swimmer swims.'])
print('Vocabulary: %s' %vect.get_feature_names())
print('Sentence 1: %s' %sentence1.toarray())
print('Sentence 2: %s' %sentence2.toarray())
哪些打印:
Vocabulary: ['.', 'like', 'swim', 'swimmer']
Sentence 1: [[1 1 1 1]]
Sentence 2: [[1 0 1 1]]
但是在第二个版本中，我怎样才能最好地去掉标点符号呢？
最佳答案
有几个选项，尝试在标记化之前删除标点符号。但这意味着 don't -> dont
import string
def tokenize(text):
text = "".join([ch for ch in text if ch not in string.punctuation])
tokens = nltk.word_tokenize(text)
stems = stem_tokens(tokens, stemmer)
return stems
或者尝试在标记化后删除标点符号。
def tokenize(text):
tokens = nltk.word_tokenize(text)
tokens = [i for i in tokens if i not in string.punctuation]
stems = stem_tokens(tokens, stemmer)
return stems
已编辑
上面的代码可以运行，但是速度很慢，因为它会多次循环相同的文本:
- 一次性删除标点符号
- 第二次标记化
- 第三次停止。
如果您有更多步骤，例如删除数字或删除停用词或小写等。
最好将这些步骤尽可能集中在一起，如果您的数据需要更多预处理步骤，这里有几个更好的答案，它们会更有效:
关于python - 结合 NLTK 和 scikit-learn 中的文本词干提取和标点符号删除，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/26126442/