Adaptive and incremental query expansion for cluster-based browsing

Eguchi, Koichi; Ito, Hidetaka; Kumamoto, Akira; Kanata, Yakichi

doi:10.1109/dasfaa.1999.765733

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…(2) and (3). Comparing the results of these two cases, we verified that the retrieval effectiveness was greatly improved by adjusting the parameters [11].…”

Section: Relevance Feedback With Adaptive Parameters Based On Documenmentioning

confidence: 54%

“…Thus, if the refined query is utilized, the ability to separate relevant and nonrelevant documents should be improved by setting [ to an adequate positive value and using Eq. (11).…”

Section: Evaluation Of Initial Clustering and Refinement Clusteringmentioning

confidence: 99%

“…The parameters in Eqs (2). and(3)are empirically set as a = 0.679, b = 0.339, * Dp k,r 0 100 and Ep k,n 1 1[2,3,11].…”

mentioning

confidence: 99%

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Adaptive document clustering using incrementally expanded queries

et al. 2001

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This paper describes a method of increasing the effectiveness of browsing by clustering large numbers of information retrieval results, and discusses the implementation of adaptive document clustering. In the proposed method, query information is successively expanded and refined in the course of browsing, and it is utilized as a clustering viewpoint that expresses the interests of the user. The proposed method is experimentally evaluated in an application to WWW information retrieval and its effectiveness is demonstrated. © 2001 Scripta Technica, Syst Comp Jpn, 32(2): 6474, 2001

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“…(2) and (3). Comparing the results of these two cases, we verified that the retrieval effectiveness was greatly improved by adjusting the parameters [11].…”

Section: Relevance Feedback With Adaptive Parameters Based On Documenmentioning

confidence: 54%

“…Thus, if the refined query is utilized, the ability to separate relevant and nonrelevant documents should be improved by setting [ to an adequate positive value and using Eq. (11).…”

Section: Evaluation Of Initial Clustering and Refinement Clusteringmentioning

confidence: 99%

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Adaptive document clustering using incrementally expanded queries

et al. 2001

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“…At this time, the last-used query will be expanded and refined using AIQEC (2] and presented to the user. He can modify the expanded query, if needed.…”

Section: Adaptivementioning

confidence: 99%

“…This method is performed as being iterated the following interactions between the system and the user: (1) the system clusters a large amount of search results, and then (2) the user judges and marks some clusters as being relevant, (3) the system merges all documents contained in the marked clusters and re-clusters them. A recently proposed method makes the information of clusters, which are judged as being relevant by the user, contribute to the query in the process of CBB to relax the loads imposed on the user [2]. It is one of relevance feedback methods [3].…”

Section: Introductionmentioning

confidence: 99%

Adaptive cluster-based browsing using incrementally expanded queries and its effects (poster abstract)

Eguchi¹

1999

Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

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The accuracy of document clustering depends on the user's viewpoints.We make use of queries as the user's viewpoints to enhance cluster-based browsing for a large amount of search results, reflecting the user's interests. Here the queries are incrementally expanded in the process of cluster-based browsing. We present an application of the proposed method to an IR system on the WWW and evaluate its performance by basic experiments.

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