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python - 如何从语料库中删除无意义或不完整的单词？

问题描述

我正在使用一些文本进行一些 NLP 分析。我已经清理了文本，采取了一些步骤来删除非字母数字字符、空格、重复词和停用词，还执行了词干提取和词形还原：

from nltk.tokenize import word_tokenize
import nltk.corpus
import re
from nltk.stem.snowball import SnowballStemmer
from nltk.stem.wordnet import WordNetLemmatizer
import pandas as pd

data_df = pd.read_csv('path/to/file/data.csv')

stopwords = nltk.corpus.stopwords.words('english') 

stemmer = SnowballStemmer('english')
lemmatizer = WordNetLemmatizer()

# Function to remove duplicates from sentence
def unique_list(l):
    ulist = []
    [ulist.append(x) for x in l if x not in ulist]
    return ulist

for i in range(len(data_df)):

    # Convert to lower case, split into individual words using word_tokenize
    sentence = word_tokenize(data_df['O_Q1A'][i].lower()) #data['O_Q1A'][i].split(' ')

    # Remove stopwords
    filtered_sentence = [w for w in sentence if not w in stopwords]

    # Remove duplicate words from sentence
    filtered_sentence = unique_list(filtered_sentence)

    # Remove non-letters
    junk_free_sentence = []
    for word in filtered_sentence:
        junk_free_sentence.append(re.sub("[^\w\s]", " ", word)) # Remove non-letters, but don't remove whitespaces just yet
        #junk_free_sentence.append(re.sub("/^[a-z]+$/", " ", word)) # Take only alphabests

    # Stem the junk free sentence
    stemmed_sentence = []
    for w in junk_free_sentence:
        stemmed_sentence.append(stemmer.stem(w))

    # Lemmatize the stemmed sentence
    lemmatized_sentence = []
    for w in stemmed_sentence:
        lemmatized_sentence.append(lemmatizer.lemmatize(w))

    data_df['O_Q1A'][i] = ' '.join(lemmatized_sentence)

但是当我显示前 10 个单词时（根据某些标准），我仍然会得到一些垃圾，例如：

ask
much
thank
work
le
know
via
sdh
n
sy
t
n t
recommend
never

在这前 10 个单词中，只有 5 个是合理的（、ask、know和recommend）。我还需要做什么才能只保留有意义的单词？thankwork

标签： pythonmachine-learningnlpdeep-learningdata-cleaning

解决方案

默认的 NLTK 停止列表是一个最小的列表，它当然不会包含诸如“询问”“多”之类的词，因为它们通常不是无意义的。这些话只与你无关，但对其他人可能无关。对于您的问题，您始终可以在使用 NLTK 后使用自定义停用词过滤器。一个简单的例子：

def removeStopWords(str):
#select english stopwords
cachedStopWords = set(stopwords.words("english"))
#add custom words
cachedStopWords.update(('ask','much','thank','etc.'))
#remove stop words
new_str = ' '.join([word for word in str.split() if word not in cachedStopWords]) 
return new_str

或者，您可以编辑 NLTK 停用词列表，它本质上是一个文本文件，存储在 NLTK 安装目录中。

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