[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fjy_s9m89gUi5CbujkC2ep9JtuYHhZSQEKdrXAOg6BZ4":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":19,"related":20,"source":31,"type":32},[],"2024-01-12 09:00:49",119650942,[8,9,10,11],"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",{"count":13,"courseId":14,"courseImg":15,"courseName":16,"workId":17,"workName":18},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","根据中文的特点以下不属于分词算法的是( )",[21,33,36,45,54,64,71,76,81,86],{"answer":22,"createTime":23,"id":24,"options":25,"question":30,"source":31,"type":32},[],"2023-12-22 17:12:10",119650940,[26,27,28,29],"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":34,"createTime":5,"id":6,"options":35,"question":19,"source":31,"type":32},[],[8,9,10,11],{"answer":37,"createTime":5,"id":38,"options":39,"question":44,"source":31,"type":32},[],119650946,[40,41,42,43],"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":46,"createTime":5,"id":47,"options":48,"question":53,"source":31,"type":32},[],119650948,[49,50,51,52],"['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":55,"createTime":56,"id":57,"options":58,"question":63,"source":31,"type":32},[],"2024-01-12 09:00:50",119650952,[59,60,61,62],"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":65,"createTime":66,"id":67,"options":68,"question":69,"source":31,"type":70},[],"2023-12-22 17:16:30",119654880,[],"常见的情感极性分析方法主要有 和 方法",2,{"answer":72,"createTime":66,"id":73,"options":74,"question":75,"source":31,"type":70},[],119654884,[],"文本相似度的检测是根据 公式进行检测",{"answer":77,"createTime":66,"id":78,"options":79,"question":80,"source":31,"type":70},[],119654889,[],"余弦相似度与向量的幅值 ,与向量的方向",{"answer":82,"createTime":66,"id":83,"options":84,"question":85,"source":31,"type":70},[],119654890,[],"文本分类属于 的机器学习",{"answer":87,"createTime":66,"id":88,"options":89,"question":90,"source":31,"type":70},[],119654891,[],"文本分类的步骤包括 、 、 、"]