Detailed Information on Publication Record

ŘEHŮŘEK, Radim. On Dimensionality of Latent Semantic Indexing for Text Segmentation. Proceedings of the International Multiconference on Computer Science and Information Technology. Wisła, Poland, 2007, vol. 2007, No 2, p. 347-356. ISSN 1896-7094.

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Basic information
Original name	On Dimensionality of Latent Semantic Indexing for Text Segmentation
Name in Czech	K dimenzionalitě Lantentního Sémantického Indexování pro segmentaci textu
Authors	ŘEHŮŘEK, Radim (203 Czech Republic, guarantor).
Edition	Proceedings of the International Multiconference on Computer Science and Information Technology, Wisła, Poland, 2007, 1896-7094.

Other information
Original language	English
Type of outcome	Article in a journal
Field of Study	10201 Computer sciences, information science, bioinformatics
Country of publisher	Poland
Confidentiality degree	is not subject to a state or trade secret
WWW	URL
RIV identification code	RIV/00216224:14330/07:00022870
Organization unit	Faculty of Informatics
Keywords in English	text segmentation; LSI; latent semantic indexing
Tags	latent semantic indexing, LSI, text segmentation
Tags	International impact, Reviewed
Changed by	Changed by: RNDr. Radim Řehůřek, Ph.D., učo 39672. Changed: 4/12/2007 00:00.

Abstract
In this paper we propose features desirable of linear text segmentation algorithms for the Information Retrieval domain, with emphasis on improving high similarity search of heterogeneous texts. We proceed to describe a robust purely statistical method, based on context overlap exploitation, that exhibits these desired features. Ways to automatically determine its internal parameter of latent space dimensionality are discussed and evaluated on a data set.

Abstract

In this paper we propose features desirable of linear text segmentation algorithms for the Information Retrieval domain, with emphasis on improving high similarity search of heterogeneous texts. We proceed to describe a robust purely statistical method, based on context overlap exploitation, that exhibits these desired features. Ways to automatically determine its internal parameter of latent space dimensionality are discussed and evaluated on a data set.

Abstract (in Czech)
In this paper we propose features desirable of linear text segmentation algorithms for the Information Retrieval domain, with emphasis on improving high similarity search of heterogeneous texts. We proceed to describe a robust purely statistical method, based on context overlap exploitation, that exhibits these desired features. Ways to automatically determine its internal parameter of latent space dimensionality are discussed and evaluated on a data set.

Abstract (in Czech)

Links
LC536, research and development project	Name: Centrum komputační lingvistiky
LC536, research and development project	Investor: Ministry of Education, Youth and Sports of the CR, Centrum komputační lingvistiky