tokens = nltk.word_tokenize(text) filtered_tokens = [word for word in tokens if word.lower() not in updated_stop_words] print(filtered_tokens) 在上述代码中,我们首先导入了NLTK库和停用词模块。然后,我们查看了NLTK默认的停用词列表。接下来,我们创建了自定义的停用词列表,并根据需要添加或删除词语。
这是我从nltk.corpus中提取停用词列表的代码: from nltk.corpus import stopwords stopWordsListEng = stopwords.words("english") 但我想添加我能想到的其他停用词: according accordingly across act actually 我还没有想出如何将它< 浏览18提问于2020-01-16得票数 0 回答已采纳 1回答 停止字删除功能的问题 、...
filtered_list=[wordforwordinexample_ifword.casefold()notinstop_words]这样就过滤掉了一些无用的单词...
fromnltk.corpusimportstopwords# 加载停用词stopwords.readme().replace('\n',' ')# 停用词说明文档,由于有很多 \n 符号,所以这样操作来方便查看''' 'Stopwords Corpus This corpus contains lists of stop words for several languages. These are high-frequency grammatical words which are usually ignored in...
然后,我们print一下,看看nltk给我们定义了什么stop word 接下来,我们就可以试试看从我们的句子里删除这些stop words~ 我们要写一个for循环,让他循环我们句子里每一个词,看看有没有出现stop word,如果不是stop word,就让他append到我们新的list里面。
from nltk.corpus import stopwords #...# stop_words = set(stopwords.words("english")) #add words that aren't in the NLTK stopwords list new_stopwords = ['apple','mango','banana'] new_stopwords_list = stop_words.union(new_stopwords) print(new_stopwords_list)import...
another_list.append(x) # it is also possible to use .remove for x in another_list: print(x,end=' ') # 2) if you want to use .remove more preferred code import sys print ("enter the string from which you want to remove list of stop words") userstring ...
stop_words = set(stopwords.words('english')) txt = "Natural language processing is an exciting area." " Huge budget have been allocated for this." tokenized = sent_tokenize(txt) for i in tokenized: wordsList = nltk.word_tokenize(i) ...
if self.stop_words.has_key(t2s): pass else: words.append(t2s) return words只需这一行代码就可以从 NLTK 获得停用词列表；并且还支持其他自然语言：nltk.corpus.stopwords.words('english')NLTK 还提供了一些 “词干分析器” 类，以便进一步规范化单词。请查看有关词干、词形归并、句子结构和语...
>>>stoplist = stopwords.words('english')# config the language name# NLTK supports 22 languages for removing the stop words>>>text="This is just a test">>>cleanwordlist = [wordforwordintext.split()ifwordnotinstoplist]# apart from just and test others are stopwords['test'] ...