Conceptual language models for domain-specific retrieval

Edgar Meij, Rudolf Berend Trieschnigg, Maarten de Rijke, Wessel Kraaij

Research output: Contribution to journalArticleAcademicpeer-review

24 Citations (Scopus)

Abstract

Over the years, various meta-languages have been used to manually enrich documents with conceptual knowledge of some kind. Examples include keyword assignment to citations or, more recently, tags to websites. In this paper we propose generative concept models as an extension to query modeling within the language modeling framework, which leverages these conceptual annotations to improve retrieval. By means of relevance feedback the original query is translated into a conceptual representation, which is subsequently used to update the query model. Extensive experimental work on five test collections in two domains shows that our approach gives significant improvements in terms of recall, initial precision and mean average precision with respect to a baseline without relevance feedback. On one test collection, it is also able to outperform a text-based pseudo-relevance feedback approach based on relevance models. On the other test collections it performs similarly to relevance models. Overall, conceptual language models have the added advantage of offering query and browsing suggestions in the form of conceptual annotations. In addition, the internal structure of the meta-language can be exploited to add related terms. Our contributions are threefold. First, an extensive study is conducted on how to effectively translate a textual query into a conceptual representation. Second, we propose a method for updating a textual query model using the concepts in conceptual representation. Finally, we provide an extensive analysis of when and how this conceptual feedback improves retrieval.
Original languageUndefined
Article number10.1016/j.ipm.2009.09.005
Pages (from-to)448-469
Number of pages22
JournalInformation processing & management
Volume47
Issue number4
DOIs
Publication statusPublished - 2010

Keywords

  • EWI-16417
  • HMI-SLT: Speech and Language Technology
  • concepts
  • Information Retrieval
  • IR-68297
  • Query modeling
  • Language modeling
  • METIS-264103
  • Meta-language

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