Query-specific clustering of search results based on document-context similarity scores

E. K.F. Dang; Wing Pong Robert Luk; D. L. Lee; K. S. Ho; S. C.F. Chan

doi:10.1145/1183614.1183780

Query-specific clustering of search results based on document-context similarity scores

E. K.F. Dang, Wing Pong Robert Luk, D. L. Lee, K. S. Ho, S. C.F. Chan

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

This paper presents a pilot study of query-specific clustering that uses our novel document-context based similarity scores as compared with document similarity scores. Clustering is applied to the top 1000 retrieved documents for a given query. Clustering effectiveness is evaluated based on the MK1 score for TREC-2, TREC-6 and TREC-7 test collections. Encouraging results were obtained whereby document-context clustering produces better MK1 scores than document clustering with a 95% confidence level if precision and recall are equally important.

Original language	English
Title of host publication	Proceedings of the 15th ACM Conference on Information and Knowledge Management, CIKM 2006
Pages	886-887
Number of pages	2
DOIs	https://doi.org/10.1145/1183614.1183780
Publication status	Published - 1 Dec 2006
Event	15th ACM Conference on Information and Knowledge Management, CIKM 2006 - Arlington, VA, United States Duration: 6 Nov 2006 → 11 Nov 2006

Conference

Conference	15th ACM Conference on Information and Knowledge Management, CIKM 2006
Country/Territory	United States
City	Arlington, VA
Period	6/11/06 → 11/11/06

Keywords

Context-based model
Document clustering
Experimentations

ASJC Scopus subject areas

General Decision Sciences
General Business,Management and Accounting

More information

10.1145/1183614.1183780

Cite this

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title = "Query-specific clustering of search results based on document-context similarity scores",

abstract = "This paper presents a pilot study of query-specific clustering that uses our novel document-context based similarity scores as compared with document similarity scores. Clustering is applied to the top 1000 retrieved documents for a given query. Clustering effectiveness is evaluated based on the MK1 score for TREC-2, TREC-6 and TREC-7 test collections. Encouraging results were obtained whereby document-context clustering produces better MK1 scores than document clustering with a 95% confidence level if precision and recall are equally important.",

keywords = "Context-based model, Document clustering, Experimentations",

author = "Dang, {E. K.F.} and Luk, {Wing Pong Robert} and Lee, {D. L.} and Ho, {K. S.} and Chan, {S. C.F.}",

year = "2006",

month = dec,

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doi = "10.1145/1183614.1183780",

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Dang, EKF, Luk, WPR, Lee, DL, Ho, KS & Chan, SCF 2006, Query-specific clustering of search results based on document-context similarity scores. in Proceedings of the 15th ACM Conference on Information and Knowledge Management, CIKM 2006. pp. 886-887, 15th ACM Conference on Information and Knowledge Management, CIKM 2006, Arlington, VA, United States, 6/11/06. https://doi.org/10.1145/1183614.1183780

Query-specific clustering of search results based on document-context similarity scores. / Dang, E. K.F.; Luk, Wing Pong Robert; Lee, D. L. et al.
Proceedings of the 15th ACM Conference on Information and Knowledge Management, CIKM 2006. 2006. p. 886-887.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AU - Chan, S. C.F.

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KW - Experimentations

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Query-specific clustering of search results based on document-context similarity scores

Abstract

Conference

Keywords

ASJC Scopus subject areas

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