Identifying frequent terms in text databases by association semantics

Yan, X; Zhang, C; Zhang, S

Identifying frequent terms in text databases by association semantics

Yan, X Zhang, C

Zhang, S

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings ITCC 2003, International Conference on Information Technology: Computers and Communications, 2003, pp. 672 - 675
Issue Date:: 2003-01-01

Closed Access

	Filename	Description	Size
	2003001881.pdf		320.61 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Yan, X	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Zhang, S	en_US
dc.date.issued	2003-01-01	en_US
dc.identifier.citation	Proceedings ITCC 2003, International Conference on Information Technology: Computers and Communications, 2003, pp. 672 - 675	en_US
dc.identifier.isbn	0769519164	en_US
dc.identifier.isbn	9780769519166	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2394
dc.description.abstract	© 2003 IEEE. Existing information retrieval methods are mainly based on either term similarity or latent semantics. To reduce irrelevant information searched, this paper presents a new approach for information retrieval by applying the methodology of association rule mining to a text database. Association semantics among terms of a document and a query are considered, such that the semantic similarity between the document and query may be reduced if they are somewhat irrelevant.	en_US
dc.relation.ispartof	Proceedings ITCC 2003, International Conference on Information Technology: Computers and Communications	en_US
dc.relation.isbasedon	10.1109/ITCC.2003.1197611	en_US
dc.title	Identifying frequent terms in text databases by association semantics	en_US
dc.type	Conference Proceeding
utslib.for	0803 Computer Software	en_US
dc.location.activity	Las Vegas, Nevada, USA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2003 IEEE. Existing information retrieval methods are mainly based on either term similarity or latent semantics. To reduce irrelevant information searched, this paper presents a new approach for information retrieval by applying the methodology of association rule mining to a text database. Association semantics among terms of a document and a query are considered, such that the semantic similarity between the document and query may be reduced if they are somewhat irrelevant.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/2394