Presentation	1999/12/20 COmparisonofRetrievaIMethodstoNewsspeech Seiichi Takao, Jun Ogata, Yasuo Ariki,
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Abstract(in English)	Recently, TV news programs are broadcast from all over the world owing to the broadcast digitization. In this situation, TV viewers want to select and watch the most interesting news. In order to satisfy this requirenlent, news database has to be constructed which has automatic topic segmentation and retrieval function, In this paper, We focus on topic retrieval among them. Conventional term weighting methods and vector space models have no applicability in spoken document retrieval because of error words caused by speech recognition. In order to solve this problem, in this paper, we propose mutual information considering TF-IDF as a new term weighting method, and word space model as a new vector space model.
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Paper #	NLC99-41
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Committee	NLC
Conference Date	1999/12/20(1days)
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Registration To	Natural Language Understanding and Models of Communication (NLC)
Language	JPN
Title (in Japanese)	(See Japanese page)
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Title (in English)	COmparisonofRetrievaIMethodstoNewsspeech
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1st Author's Name	Seiichi Takao
1st Author's Affiliation	()
2nd Author's Name	Jun Ogata
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3rd Author's Name	Yasuo Ariki
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Date	1999/12/20
Paper #	NLC99-41
Volume (vol)	vol.99
Number (no)	523
Page	pp.pp.-
#Pages	6
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