Presentation | 2004/11/27 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) TORU ISHIBASHI, YASUFUMI TAKAMA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | 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 and 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. |
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Paper # | AI2004-19 |
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Committee | AI |
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Conference Date | 2004/11/27(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Artificial Intelligence and Knowledge-Based Processing (AI) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | 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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1st Author's Name | TORU ISHIBASHI |
1st Author's Affiliation | Tokyo Metropolitan Institue of Technology() |
2nd Author's Name | YASUFUMI TAKAMA |
2nd Author's Affiliation | Tokyo Metropolitan Institue of Technology |
Date | 2004/11/27 |
Paper # | AI2004-19 |
Volume (vol) | vol.104 |
Number (no) | 485 |
Page | pp.pp.- |
#Pages | 6 |
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