Incremental relevance feedback in Japanese text retrieval

Gareth Jones, Tetsuya Sakai, Masahiro Kajiura, Kazuo Sumita

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The application of relevance feedback techniques has been shown to improve retrieval performance for a number of information retrieval tasks. This paper explores incremental relevance feedback for ad hoc Japanese text retrieval; examining, separately and in combination, the utility of term reweighting and query expansion using a probabilistic retrieval model. Retrieval performance is evaluated in terms of standard precision-recall measures, and also using "number-to-view" graphs. Experimental results, on the standard BMIR-J2 Japanese language retrieval collection, show that both term reweighting and query expansion improve retrieval performance. This is reflected in improvements in both precision and recall, but also a reduction in the average number of documents which must be viewed to find a selected number of relevant items. In particular, using a simple simulation of user searching, incremental application of relevance information is shown to lead to progressively improved retrieval performance and an overall reduction in the number of documents that a user must view to find relevant ones.

Original languageEnglish
Pages (from-to)361-384
Number of pages24
JournalInformation Retrieval
Volume2
Issue number4
DOIs
Publication statusPublished - 2000 Jan 1
Externally publishedYes

Keywords

  • Incremental relevance feedback
  • Japanese text
  • Number-to-view graphs
  • Probabilistic retrieval
  • Query expansion
  • Term reweighting

ASJC Scopus subject areas

  • Information Systems
  • Library and Information Sciences

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