Proposal of M2VSM and Its Comparison with Conventional VSM(Text Mining I)

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概要

• 論文の詳細を見る

• Information retrieval based on Vector Space Model (VSM) only employs typical indexing terms contained in documents. For that reason, when we apply it to a specific field such as medicine, it can crowd the documents in the vector space, which makes it difficult to retrieve and cluster them. In this paper, modified VSM based on meta keywords such as adjectives arid adverbs, which is called M2VSM (Meta keyword-based Modified VSM), is proposed for separating the crowded documents using meta keywords as additional value of indexing terms. Experimental results by applying M2VSM to Medline (medical literature database) show that it can separate documents crowded in the vector space.

• 一般社団法人情報処理学会の論文
• 2004-12-04

著者

• Ishibashi Toru

  Tokyo Metropolitan Institue Of Technology

• TAKAMA YASUFUMI

  Tokyo Metropolitan Institue of Technology

関連論文

• Proposal of M2VSM and Its Comparison with Conventional VSM(Text Mining I)
• Proposal of M2VSM and Its Comparison with Conventional VSM(Text Mining I)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)

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