from nltk.corpus import stopwords stop_words = set(stopwords.words('english')) # 英文停用词列表 def remove_stopwords(file_paths): for file_path in file_paths: with open(file_path, 'r') as file: text = file.read() words = text.split() filtered_words = [word for word in words if...
我们可以从类gensim.parsing.preprocessing轻松导入remove_stopwords方法。 尝试使用Gensim去除停用词: # 以下代码使用Gensim去除停用词from gensim.parsing.preprocessing import remove_stopwords# pass the sentence in the remove_stopwords functionresult = remove_stopwords("""He determined to drop his litigation with ...
fromnltk.corpusimportstopwordsfromnltk.tokenizeimportword_tokenize stop_words=set(stopwords.words('english'))text="This is an example of a sentence where we want to remove all the stopwords."tokenized_text=word_tokenize(text)filtered_text=[wordforwordintokenized_textifnotwordinstop_words]print(filt...
复制代码 去除停用词(Remove Stopwords):停用词是在文本处理过程中无意义的词语,比如“a”、“the”等。可以使用NLTK的stopwords来去除停用词。 from nltk.corpus import stopwords stop_words = set(stopwords.words('english')) filtered_words = [word for word in tokens if word.lower() not in stop_words...
{', '}']) # remove it if you need punctuation for doc in documents: list_of_words = [i.lower() for i in wordpunct_tokenize(doc) if i.lower() not in stop_words]请注意,由于您在这里搜索集合(不在列表中),因此理论上速度会len(stop_words)/2快一些,如果您需要通过...
import sys print ("enter the string from which you want to remove list of stop words") userstring = input().split(" ") list =["a","an","the","in"] another_list = [] for x in userstring: if x not in list: # comparing from the list and removing it another_list...
stop_words = set(stopwords.words('english')) 定义一个函数,用于删除不在NLTK停用词库中的停用词: 代码语言:txt 复制 def remove_stopwords(text): tokens = text.split() filtered_tokens = [word for word in tokens if word.lower() not in stop_words] return ' '.join(filtered_tokens) ...
.tokenizeimportword_tokenize# Load the NLTK stop wordsstop_words=set(stopwords.words('english'))text="NLTK is a leading platform for building Python programs to work with human language data."tokens=word_tokenize(text)# Remove stop wordsfiltered_tokens=[wforwintokensifnotwinstop_words]print(...
for word in word_list: # iterate over word_list if word in stopwords.words('english'):filtered_word_list.remove(word) # remove word from filtered_word_list if it is a stopword 3. 你也可以做一组差异，例如：list(set(nltk.regexp_tokenize(sentence, pattern, gaps=True)) - ...
from nltk.corpus import stopwords# ...filtered_words = [word for word in word_list if word ...