Question

我正在清理data frame中的Sumcription栏中的内容，并尝试执行3件事：

标记化
合法化

删除停用词

import spacy        
nlp = spacy.load('en_core_web_sm', parser=False, entity=False)        
df['Tokens'] = df.Sumcription.apply(lambda x: nlp(x))    
spacy_stopwords = spacy.lang.en.stop_words.STOP_WORDS        
spacy_stopwords.add('attach')
df['Lema_Token']  = df.Tokens.apply(lambda x: " ".join([token.lemma_ for token in x if token not in spacy_stopwords]))

但是，例如，当我打印时：

df.Lema_Token.iloc[8]

输出中仍然包含attach这个词： attach poster on the wall because it is cool

为什么不删除停用词？

我也尝试过：

df['Lema_Token_Test']  = df.Tokens.apply(lambda x: [token.lemma_ for token in x if token not in spacy_stopwords])

但是str attach仍然出现。

Answer 1

import spacy
import pandas as pd

# Load spacy model
nlp = spacy.load('en', parser=False, entity=False)        

# New stop words list 
customize_stop_words = [
    'attach'
]

# Mark them as stop words
for w in customize_stop_words:
    nlp.vocab[w].is_stop = True


# Test data
df = pd.DataFrame( {'Sumcription': ["attach poster on the wall because it is cool",
                                   "eating and sleeping"]})

# Convert each row into spacy document and return the lemma of the tokens in 
# the document if it is not a sotp word. Finally join the lemmas into as a string
df['Sumcription_lema'] = df.Sumcription.apply(lambda text: 
                                          " ".join(token.lemma_ for token in nlp(text) 
                                                   if not token.is_stop))

print (df)

输出：

   Sumcription                                   Sumcription_lema
0  attach poster on the wall because it is cool  poster wall cool
1                           eating and sleeping         eat sleep

使用spacy删除停用词

1 个答案: