[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f742hjK2jZc-8d5rO-3SZSBYHj7o15AjXVVoySJrIKPQ":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":8,"question":15,"related":16,"source":27,"type":72},[],"2023-12-22 17:16:30",119654889,[],{"count":9,"courseId":10,"courseImg":11,"courseName":12,"workId":13,"workName":14},18,"5bb33fcfdc6763436f5e0606d1aa35fa","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F491c0674c934738c875ce5ce8e90e815.png","商务数据分析与应用","work_31741195","第23章 文本数据分析","余弦相似度与向量的幅值 ,与向量的方向",[17,29,39,48,57,67,73,78,81,86],{"answer":18,"createTime":19,"id":20,"options":21,"question":26,"source":27,"type":28},[],"2023-12-22 17:12:10",119650940,[22,23,24,25],"nltk是一个免费的、开源的、社区驱动的项目","nltk擅长处理英文文本","nltk库只能处理英文文本","nltk包括分词、词性标注、命名实体识别及句法分析等","下列选项中,关于nltk库的描述不正确的是( )","v1",0,{"answer":30,"createTime":31,"id":32,"options":33,"question":38,"source":27,"type":28},[],"2024-01-12 09:00:49",119650942,[34,35,36,37],"基于动态的分词方法","基于规则的分词方法","基于理解的分词方法","基于统计的分词方法","根据中文的特点以下不属于分词算法的是( )",{"answer":40,"createTime":31,"id":41,"options":42,"question":47,"source":27,"type":28},[],119650946,[43,44,45,46],"支持繁体分词模式","支持精确模式","支持全模式","支持搜索引擎模式","下列选项中,不属于jieba分词模式( )",{"answer":49,"createTime":31,"id":50,"options":51,"question":56,"source":27,"type":28},[],119650948,[52,53,54,55],"['Life', 'short', ',', 'need', 'Python', '.']","['Life', 'short', ',', 'you' ,'need', 'Python', '.']","['Life', 'short', 'need', 'Python',]","['Life', 'is', 'short', ',', 'need', 'Python', '.']","阅读下面一段程序: from nltk.corpus import stopwords import nltk sentence = 'Life is short,you need Python.' words = nltk.word_tokenize(sentence) stop_words = stopwords.words('english') remain_words = [] for word in words: if word not in stop_words: remain_words.append(word) print(remain_words) 执行上述程序,最终输出的结果为( )",{"answer":58,"createTime":59,"id":60,"options":61,"question":66,"source":27,"type":28},[],"2024-01-12 09:00:50",119650952,[62,63,64,65],"人生 苦短 我用 Pyhton","人 生 苦 短 我 用 Pyhton","人生 苦短 我 用 Pyhton","人生苦短 我 用 Pyhton","阅读下面一段程序: import jieba sentence = '人生苦短,我用Pyhton' terms_list = jieba.cut(sentence, cut_all=True) print(' '.join(terms_list)) 执行上述程序,最终输出的结果为( )",{"answer":68,"createTime":5,"id":69,"options":70,"question":71,"source":27,"type":72},[],119654880,[],"常见的情感极性分析方法主要有 和 方法",2,{"answer":74,"createTime":5,"id":75,"options":76,"question":77,"source":27,"type":72},[],119654884,[],"文本相似度的检测是根据 公式进行检测",{"answer":79,"createTime":5,"id":6,"options":80,"question":15,"source":27,"type":72},[],[],{"answer":82,"createTime":5,"id":83,"options":84,"question":85,"source":27,"type":72},[],119654890,[],"文本分类属于 的机器学习",{"answer":87,"createTime":5,"id":88,"options":89,"question":90,"source":27,"type":72},[],119654891,[],"文本分类的步骤包括 、 、 、"]