Measuring concept relatedness using language models

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.

REFERENCES

• 1: Jorge E. Caviedes , James J. Cimino, Towards the development of a conceptual distance metric for the UMLS, Journal of Biomedical Informatics, v.37 n.2, p.77-85, April 2004 [doi>10.1016/j.jbi.2004.02.001]
• 2: W. Kraaij. Variations on Language Modeling for Information Retrieval. PhD thesis, University of Twente, June 2004.
• 3: Dekang Lin, An Information-Theoretic Definition of Similarity, Proceedings of the Fifteenth International Conference on Machine Learning, p.296-304, July 24-27, 1998
• 4: Christopher D. Manning , Hinrich Schütze, Foundations of statistical natural language processing, MIT Press, Cambridge, MA, 1999
• 5: H. Nguyen and H. Al-Mubaid. New ontology-based semantic similarity measure for the biomedical domain. In 2006 IEEE Int. Conf. on Granular Computing, pages 623--628, 2006.
• 6: Ted Pedersen , Serguei V. S. Pakhomov , Siddharth Patwardhan , Christopher G. Chute, Measures of semantic similarity and relatedness in the biomedical domain, Journal of Biomedical Informatics, v.40 n.3, p.288-299, June, 2007 [doi>10.1016/j.jbi.2006.06.004]
• 7: P. Resnik. Semantic similarity in a taxonomy. Journal of Artificial Intelligence Research, 11:95--130, 1999.
• 8: C. J. Van Rijsbergen, Information Retrieval, Butterworth-Heinemann, Newton, MA, 1979
• 9: D. Trieschnigg and W. Kraaij Hierarchical topic detection in large digital news archives: Exploring a sample based approach. Journal of Digital Information Management, 3(1): 21--26, 2005.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Abstracting methods

General Terms:
Algorithms, Experimentation, Measurement, Theory

Keywords:
language models, semantic relatedness