我们在语料库中有 10 个文档，即 d1, d2, ..., d10。 你将算出下列单词和文档中的 IDF 和 TF-IDF（词频-逆文档频率）值。 单词“机器”在 d1 中出现 10 次，出现在 5 个文档中。 单词“学习”在 d2 中出现 8 次，出现在 2 个文档中。 We have 10 documents in the corpus, d1, d2, ..., d10. You will calculate the IDF and TF-IDF (Term Frequency-Inverse Document Frequency) values for the following words and documents. Word "machine" appears  10 times in d1 and appears in 5 documents. Word "learning" appears 8 times in d2 and appears in 2 documents. 1. “机器”的 IDF（逆文档频率）值是多少？ What is the IDF (Inverse Document Frequency) value of "machine"? idfj =  [選擇] 0 1 2 4 5   2. “学习”的 TF-IDF（词频-逆文档频率）值是多少？ What is the TF-IDF (Term Frequency-Inverse Document Frequency) value of "learning"? tf-idf("learning", d2)  = [選擇] log2(5) 10*log2(5) 8*log2(5) 2*log2(5)  多重下拉选择题

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When analyzing articles, the tf-idf-tf_idf framework is used to:

By vectorizing text using TF-IDF approach we lose some information contained in the raw document:

The TF-IDF approach considers information about the occurrences of tokens in all documents of a text corpus:

The term frequency - inverse document frequency (TF-IDF) approach to text vectorization is based on the bag-of-words representation:

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