如何从语料库中删除无意义或不完整的单词?

Question

我正在使用一些文本进行一些NLP分析。我已经清理了文本，采取措施删除了非字母数字字符、空格、重复词和停用词，并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("[^ws]", " ", 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(根据某些标准(时，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个词中，只有5个是明智的(ask、know、recommend、thank和work(。我还需要做什么才能只保留有意义的单词？

Answer 1

默认的 NLTK 停用列表是最小的，它当然不会包含像"问"很多"这样的词，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，在使用 NLTK 后，您始终可以使用自定义非索引字筛选器。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 非索引字列表，该列表实质上是一个文本文件，存储在 NLTK 安装目录中。