Measuring concept relatedness using language models

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

7 Citations (Scopus)

Abstract

Over the years, the notion of concept relatedness has attracted considerable attention. A variety of approaches, based on ontology structure, information content, association, or context have been proposed to indicate the relatedness of abstract ideas. We propose a method based on the cross entropy reduction between language models of concepts which are estimated based on document-concept assignments. The approach shows improved or competitive results compared to state-of-the-art methods on two test sets in the biomedical domain.
Original languageUndefined
Title of host publicationProceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
EditorsS.H. Myaeng, W.D. Oard, F. Sebastiani, M.K. Leong
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages823-824
Number of pages2
ISBN (Print)978-1-60558-164-4
DOIs
Publication statusPublished - 2008
Event31st Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008 - Singapore, Singapore
Duration: 20 Jul 200825 Jul 2008
Conference number: 31

Publication series

NameAnnual ACM Conference on Research and Development in Information Retrieval
PublisherACM
Number10
Volume31

Conference

Conference31st Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2008
Abbreviated titleSIGIR
CountrySingapore
CitySingapore
Period20/07/0825/07/08

Keywords

  • METIS-252101
  • EWI-14032
  • IR-62526

Cite this

Trieschnigg, R. B., Meij, E., de Rijke, M., & Kraaij, W. (2008). Measuring concept relatedness using language models. In S. H. Myaeng, W. D. Oard, F. Sebastiani, & M. K. Leong (Eds.), Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (pp. 823-824). [10.1145/1390334.1390523] (Annual ACM Conference on Research and Development in Information Retrieval; Vol. 31, No. 10). New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/1390334.1390523