Question

我是Python文本处理的新手，我试图在文本文档中使用词干，大约有5000行。

我写了以下脚本

from nltk.corpus import stopwords # Import the stop word list
from nltk.stem.snowball import SnowballStemmer

stemmer = SnowballStemmer('english')

def Description_to_words(raw_Description ):
    # 1. Remove HTML
    Description_text = BeautifulSoup(raw_Description).get_text() 
    # 2. Remove non-letters        
    letters_only = re.sub("[^a-zA-Z]", " ", Description_text) 
    # 3. Convert to lower case, split into individual words
    words = letters_only.lower().split()                       

    stops = set(stopwords.words("english"))                  
    # 5. Remove stop words
    meaningful_words = [w for w in words if not w in stops]   
    # 5. stem words
    words = ([stemmer.stem(w) for w in words])

    # 6. Join the words back into one string separated by space, 
    # and return the result.
    return( " ".join( meaningful_words ))   

clean_Description = Description_to_words(train["Description"][15])

但是当我测试结果时，单词没有被阻止，任何人都可以帮助我知道什么是问题，我在“Description_to_words”函数中做错了什么

而且，当我像下面一样单独执行stem命令时，它可以工作。

from nltk.tokenize import sent_tokenize, word_tokenize
>>> words = word_tokenize("MOBILE APP - Unable to add reading")
>>> 
>>> for w in words:
...     print(stemmer.stem(w))
... 
mobil
app
-
unabl
to
add
read

Answer 1

这是你的功能的每一步，都已修复。

删除HTML。

Description_text = BeautifulSoup(raw_Description).get_text()

删除非字母，但暂时不删除空格。你也可以简化一下你的正则表达式。
```
letters_only = re.sub("[^\w\s]", " ", Description_text) 
```

转换为小写，分成单个单词：我建议再次使用word_tokenize。

from nltk.tokenize import word_tokenize
words = word_tokenize(letters_only.lower())

删除停用词。

stops = set(stopwords.words("english")) 
meaningful_words = [w for w in words if not w in stops]

词干。这是另一个问题。词干meaningful_words，而非words。
```
return ' '.join(stemmer.stem(w) for w in meaningful_words])
```

用NLTK（python）词干

1 个答案: