中国大学MOOC:下面是一段文档的向量化的程序，且未经停用词过滤fromsklearn.feature_extraction.textimportCountVectorizercorpus=[JobswasthechairmanofAppleInc.,andhewasveryfamous,Iliketouseapplecomputer,AndIalsoliketoeatapple]vectorizer=CountVectorizer()print(vectorizer.vocabulary_)print(vectorizer.fit_transform(corpus).todense())#转化为完整特征矩阵已知print(vectorizer.vocabulary_)的输出结果为：{uand:1,ujobs:9,uapple:2,uvery:15,ufamous:6,ucomputer:4,ueat:5,uhe:7,uuse:14,ulike:10,uto:13,uof:11,ualso:0,uchairman:3,uthe:12,uinc:8,uwas:16}.则最后一条print语句中文档D1，即JobswasthechairmanofAppleInc.,andhewasveryfamous的向量为

中国大学MOOC: 下面是一段文档的向量化的程序，且未经停用词过滤from sklearn.feature_extraction.text import CountVectorizercorpus = [Jobs was the chairman of Apple Inc., and he was very famous,I like to use apple computer,And I also like to eat apple] vectorizer =CountVectorizer()print(vectorizer.vocabulary_)print(vectorizer.fit_transform(corpus).todense()) #转化为完整特征矩阵已知print(vectorizer.vocabulary_)的输出结果为：{uand: 1, ujobs: 9, uapple: 2, uvery: 15, ufamous: 6, ucomputer: 4, ueat: 5, uhe: 7, uuse: 14, ulike: 10, uto: 13, uof: 11, ualso: 0, uchairman: 3, uthe: 12,

vocabulary and structure

Vocabulary Work-B

Job Seeking_ Vocabulary Match_122(副本) Match the following vocabulary items with their definitions, referring to the Text for clues.

A good vocabulary is like an _________ _______.

vocabulary请勿打扰牌:______

Vocabulary can be region_____.

The vocabulary in this () is taught in a meaningful context.

How large is the English vocabulary?