例如,在Python中可以使用NLTK库的stopwords模块来移除停用词: ```python from nltk.corpus import stopwords stop_words = stopwords.words('english') text = 'This is an example sentence, showing off stop words filtration.' clean_text = ' '.join([word for word in text.split() if word.lower() ...
# 需要导入模块: from nltk.probability import FreqDist [as 别名]# 或者: from nltk.probability.FreqDist importremove[as 别名]deffindKeyword(fname,apply=False,eventflg =False):withopen(fname ,'r', encoding='utf-8', errors='ignore')asfile :#Opening filetext=file.read().lower()#finding toke...
"nltk.download('wordnet')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Lemmatize the tokens\n", "from nltk.stem.wordnet import WordNetLemmatizer\n", "\n", "lem = WordNetLemmatizer()\n", "train_tokens_lem = ...
import nltk nltk.download('stopwords') 复制 它将下载一个带有英文停用词的文件。 验证停用词 from nltk.corpus import stopwords stopwords.words('english') print stopwords.words() [620:680] 复制 当我们运行上述程序时,我们得到以下输出 - [u'your', u'yours', u'yourself', u'yourselves', u...