Presentation | 2006-05-18 A study of hot topic extraction from document stream based on probabilistic models Manabu KIMURA, Kazumi SAITO, Naonori UEDA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In this paper, we address the task of extracting hot topics from document streams such as a series of news papers. To this end, we present a method based on a bursty appearance of words, as a variant of the Kleinberg's method based on a bursty appearance of documents. In our experiments using news papers during one year, we report that the proposed method showed a better extraction performance on a set of benchmark topics extracted by humans, in comparson to the Kleinberg's original method. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | hot topic extraction / document stream / burst / probabilistic model |
Paper # | AI2006-10 |
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Committee | AI |
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Conference Date | 2006/5/11(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Artificial Intelligence and Knowledge-Based Processing (AI) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | A study of hot topic extraction from document stream based on probabilistic models |
Sub Title (in English) | |
Keyword(1) | hot topic extraction |
Keyword(2) | document stream |
Keyword(3) | burst |
Keyword(4) | probabilistic model |
1st Author's Name | Manabu KIMURA |
1st Author's Affiliation | Graduate School of Information Science, Nara Institute of Science and Technology() |
2nd Author's Name | Kazumi SAITO |
2nd Author's Affiliation | NIPPON TELEGRAPH AND TELEPHONE CORPORATION, NTT Communication Science Laboratories |
3rd Author's Name | Naonori UEDA |
3rd Author's Affiliation | NIPPON TELEGRAPH AND TELEPHONE CORPORATION, NTT Communication Science Laboratories |
Date | 2006-05-18 |
Paper # | AI2006-10 |
Volume (vol) | vol.106 |
Number (no) | 38 |
Page | pp.pp.- |
#Pages | 6 |
Date of Issue |