read() # 断句 sentences = sent_tokenize(corpus) # 分词 words = word_tokennize(corpus) 2. 停用词 一些文本,对于最后的任务目标并没有帮助,甚至带来一些干扰。我们把这类词称为停用词,比如定冠词/不定冠词等 # 导入内置停用词 from nltk.corpus import stopwords stop_words = stopwords.words('english'...
from nltk.corpus import stopwordsfrom nltk.tokenize import word_tokenize import nltkfrom nltk.stem import WordNetLemmatizerset(stopwords.words('english'))text = """He determined to drop his litigation with the monastry, and relinguish his claims to the wood-cuting and fishery rihgts at once. ...
- 去除停用词：过滤掉常见的、不提供实际意义的词汇。from nltk.corpus import stopwords filtered_text = [word for word in tokens if word not in stopwords.words('english')]print(filtered_text)3. 高级文本分析 - 情感分析：评估文本的情感倾向，如积极、消极或中立。from nltk.sentiment.vader import Sent...
首先我import stopwords进来,代码如下: from nltk.corpus import stopwords words = stopwords.words('english') print(words) 首先看看打印停用词的结果: ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', ...
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、句子结构和语法的 NLTK 文档，了解有关的更多信息。使用 Naive Bayes 算法进行分类 算法在 NLTK 中被广泛使用并利用 nltk.NaiveBayesClassifier 类实现。Bayes 算法根据特性在其数据集中...
nltk.download('stopwords') 读取停用词数据集 下载完成后,我们可以使用nltk库中的corpus()函数读取停用词数据集。这里我们分别展示如何读取英文和中文停用词数据集: # 读取英文停用词数据集 stop_words_english = set(stopwords.words('english')) # 读取中文停用词数据集(注意:nltk默认可能不包含完整的中文停用词...
stopwords即是,遗憾的是没有中文停用词 fromnltk.corpusimportstopwords#定义一个计算func计算不在停用词列表中的比例的函数defcontent(text): stopwords_eng = stopwords.words('english') content = [wforwintextifw.lower()andwnotinstopwords_eng]returnlen(content)/len(text)print(content(nltk.corpus.reuters....
from nltk.corpus import stopwords from nltk.tokenize import word_tokenize from nltk.stem import WordNetLemmatizer # 下载NLTK数据包 nltk.download('punkt') nltk.download('stopwords') nltk.download('wordnet') # 示例文本 text = "Natural language processing (NLP) is a field of artificial intelligen...
from nltk.corpusimportstopwords stopwords.fileids()# 具体的语言 javascript ### 果然没有中文['arabic','azerbaijani','danish','dutch','english','finnish','french','german','greek','hungarian','italian','kazakh','nepali','norwegian','portuguese','romanian','russian','spanish','swedish','...
from nltk.corpus import stopwords import nltk disease_List = nltk.word_tokenize(text) #去除停用词 filtered = [w for w in disease_List if(w not in stopwords.words('english')] #进行词性分析,去掉动词、助词等 Rfiltered =nltk.pos_tag(filtered) ...