python - 用 NLTK (python) 词干词干

Question

我是 Python 文本处理的新手，我正在尝试提取文本文档中的单词，大约有 5000 行。

我写了下面的脚本

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])

但是当我测试结果单词没有词干时，任何人都可以帮助我知道问题是什么，我在“Description_to_words”函数中做错了

而且，当我像下面这样单独执行 stem 命令时，它会起作用。

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

这是您的函数的每个步骤，已修复。

1. 删除 HTML。

   Description_text = BeautifulSoup(raw_Description).get_text() 
   

2. 删除非字母，但暂时不要删除空格。您还可以稍微简化您的正则表达式。

   letters_only = re.sub("[^\w\s]", " ", Description_text) 
   

3. 转换为小写，拆分为单个单词:我建议再次使用 word_tokenize，在这里。

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

4. 删除停用词。

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

5. 词干。这是另一个问题。词干 meaningful_words，而不是 words。

   return ' '.join(stemmer.stem(w) for w in meaningful_words])