Detailed Information on Publication Record

ŘEHŮŘEK, Radim. Text Segmentation Using Context Overlap. Progress in Artificial Intelligence, Guimarães, Portugal: Springer Berlin / Heidelberg, 2007, vol. 2007, No 4874, p. 647-658. ISSN 0302-9743.
Other formats: BibTeX LaTeX RIS @article{745015, author = {Řehůřek, Radim}, article_location = {Guimarães, Portugal}, article_number = {4874}, keywords = {text segmentation; LSI; latent semantic indexing}, language = {eng}, issn = {0302-9743}, journal = {Progress in Artificial Intelligence}, title = {Text Segmentation Using Context Overlap}, url = {http://www.springerlink.com/content/k820g107h7067383/?p=9cc314a6a70b4ca286722b609e097494&pi=0}, volume = {2007}, year = {2007} } TY - JOUR ID - 745015 AU - Řehůřek, Radim PY - 2007 TI - Text Segmentation Using Context Overlap JF - Progress in Artificial Intelligence VL - 2007 IS - 4874 SP - 647-658 EP - 647-658 PB - Springer Berlin / Heidelberg SN - 03029743 KW - text segmentation KW - LSI KW - latent semantic indexing UR - http://www.springerlink.com/content/k820g107h7067383/?p=9cc314a6a70b4ca286722b609e097494&pi=0 N2 - 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. Experimental results are presented, along with comparison to other existing algorithms. ER - ŘEHŮŘEK, Radim. \textit{Text Segmentation Using Context Overlap}. \textit{Progress in Artificial Intelligence}, Guimarães, Portugal: Springer Berlin / Heidelberg, 2007, vol.~2007, No~4874, p.~647-658. ISSN~0302-9743.

ŘEHŮŘEK, Radim. Text Segmentation Using Context Overlap. Progress in Artificial Intelligence, Guimarães, Portugal: Springer Berlin / Heidelberg, 2007, vol. 2007, No 4874, p. 647-658. ISSN 0302-9743.

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Basic information
Original name	Text Segmentation Using Context Overlap
Name in Czech	Segmentace textu s použitím překryvu kontextů
Authors	ŘEHŮŘEK, Radim (203 Czech Republic, guarantor).
Edition	Progress in Artificial Intelligence, Guimarães, Portugal, Springer Berlin / Heidelberg, 2007, 0302-9743.

Other information
Original language	English
Type of outcome	article in a journal
Field of Study	10201 Computer sciences, information science, bioinformatics
Country of publisher	Portugal
Confidentiality degree	is not subject to a state or trade secret
WWW	URL
RIV identification code	RIV/00216224:14330/07:00023050
Organization unit	Faculty of Informatics
UT WoS	000252074800054
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: 29/3/2010 18:51.

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. Experimental results are presented, along with comparison to other existing algorithms.

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. Experimental results are presented, along with comparison to other existing algorithms.

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. Experimental results are presented, along with comparison to other existing algorithms.

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, Basic Research Center