Measures of semantic similarity and relatedness in the biomedical domain

Journal of Biomedical Informatics

Volumn 40, Issue 3, 2007, Pages 288-299

  Pedersen, Ted ^a   Pakhomov, Serguei V S ^b   Patwardhan, Siddharth ^c   Chute, Christopher G ^b  

^a UNIVERSITY OF MINNESOTA DULUTH   (United States)

^b Mayo Clinic   (United States)

^c Department of Electrical and Computer Engineering   (United States)

Author keywords

Context vectors; Information content; Path based measures; Semantic similarity; SNOMED CT

Indexed keywords

DATABASE SYSTEMS; INFORMATION ANALYSIS; NATURAL LANGUAGE PROCESSING SYSTEMS; ONTOLOGY; SEMANTICS; STATISTICS;

CONTEXT VECTORS; INFORMATION CONTENT; PATH BASED MEASURES; SEMANTIC SIMILARITY; SNOMED-CT;

BIOMEDICAL ENGINEERING;

ARTICLE; BIOMEDICINE; INFORMATION SYSTEM; NOMENCLATURE; PHYSICIAN; PRIORITY JOURNAL; SCORING SYSTEM; SEMANTICS;

DATABASE MANAGEMENT SYSTEMS; DATABASES, FACTUAL; FORMS AND RECORDS CONTROL; HUMANS; INFORMATION STORAGE AND RETRIEVAL; LANGUAGE; MEDICAL INFORMATICS; MEDICAL RECORDS SYSTEMS, COMPUTERIZED; NATURAL LANGUAGE PROCESSING; SEMANTICS; SOFTWARE; SYSTEMATIZED NOMENCLATURE OF MEDICINE; TERMINOLOGY; VOCABULARY, CONTROLLED;

EID: 34248172904     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2006.06.004     Document Type: Article

References (38)

1. Rubenstein H., and Goodenough J. Contextual correlates of synonymy. Communications of the ACM 8 (1965) 627-633
2. Miller G., and Charles W. Contextual correlates of semantic similarity. Language and Cognitive Processes 6 1 (1991) 1-28
3. Resnik P. Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th international joint conference on artificial intelligence. Montreal, Canada; 1995. p. 448-53.
4. Lin D. An information-theoretic definition of similarity. Proceedings of the 15th International Conference on Machine Learning (1998), Madison, WI p. 296-304
5. Jiang J, Conrath D. Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of the 10th international conference on research in computational linguistics, Taipei, Taiwan; 1997. p. 19-33.
6. Patwardhan S, Pedersen T. Using WordNet-based context vectors to estimate the semantic relatedness of concepts. In: Proceedings of the EACL 2006 workshop, making sense of sense: Bringing computational linguistics and psycholinguistics together. Trento, Italy; 2006. p. 1-8.
7. Rosario B, Hearst M. Classifying semantic relations in bioscience texts. In: Proceedings of the 42nd annual meeting of the association for computational linguistics. Barcelona, Spain; 2004. p. 430-7.
8. Budanitsky A, Hirst G. Semantic distance in WordNet: an experimental application oriented evaluation of five measures. In: Proceedings of the NACCL 2001 Workshop: on WordNet and other lexical resources: Applications, extensions, and customizations. Pittsburgh, PA; 2001. p. 29-34.
9. Resnik P. WordNet and class-based probabilities. In: Fellbaum C. (Ed). WordNet: An electronic lexical database (1998), MIT Press, Cambridge, MA 239-263
10. Patwardhan S, Banerjee S, Pedersen T. Using measures of semantic relatedness for word sense disambiguation. In: Proceedings of the fourth international conference on intelligent text processing and computational linguistics. Mexico City, Mexico; 2003. p. 241-57.
11. McCarthy D, Keoling R, Weeds J, Carroll J. Finding predominant word senses in untagged text. In: Proceedings of the 42nd meeting of the association for computational linguistics. Barcelona, Spain; 2004. p. 276-86.
12. In: Fellbaum C. (Ed). WordNet: An electronic lexical database (1998), MIT Press, Cambridge, MA
13. Rada R, Mili H, Bicknell E, Blettner M. Development and application of a metric on semantic nets. In: IEEE transactions on systems, man and cybernetics, 1989;19(1): p. 17-30.
14. Caviedes J., and Cimino J. Towards the development of a conceptual distance metric for the UMLS. J Biomed Informatics 37 (2004) 77-85
15. Lord P., Stevens R., Brass A., and Goble C. Investigating semantic similarity measures across the gene ontology: the relationship between sequence and annotation. Bioinformatics 19 10 (2003) 1275-1283
16. The Gene Ontology Consortium, Gene Ontology: Tool for the Unification of Biology. Nat Genet 2000;25:25-9.
17. Wilbur W., and Yang Y. An analysis of statistical term strength and its use in the indexing and retrieval of molecular biology texts. Comput Biol Med 26 (1996) 209-222
18. Spasic I, Ananiadou S. A flexible measure of contextual similarity for biomedical terms, Pacific Biocomputing Symposium 2005;10:197-208.
19. Levenshtein V. Binary codes capable of correcting deletions, insertions and reversals. Sov Phys Dokl 10 (1966) 707-710
20. Schütze H. Automatic word sense discrimination. Comput Linguist 24 1 (1998) 97-123
21. Deerwester S., Dumais S., Furnas G., Landauer T., and Harshman R. Indexing by latent semantic analysis. J Am Soc Inf Sci 41 (1990) 391-407
22. Chute C. Classification and retrieval of patient records using natural language: an experimental application of latent semantic analysis. In: Proceedings of the annual international conference of the IEEE engineering in medicine and biology society. Orlando, FL; 1991. p. 1162-3.
23. Chute C, Yang Y. An evaluation of concept-based latent semantic indexing for clinical information retrieval. In: Proceedings of the 16th annual symposium on computer applications in medical care. Baltimore, MD; 1992. p. 639-43.
24. Chute C. The classification of medical events using latent semantic analysis. In: Advances in classification research vol. 2: Proceedings of the second ASIS SIG/CR workshop on classification research. Medford, NJ; 1992. p. 45-51.
25. Chute C, Yang Y, Evans D. Latent semantic indexing of medical diagnoses using UMLS semantic structures. In: Proceedings of the 15th annual symposium on computer applications in medical care. New York City, NY; 1991. p. 185-9.
26. Evans D, Chute C, Handerson S, Yang Y, Monardch I, Hersh W. Latent semantics as a basis for managing variation in medical terminologies. In: Proceedings of the seventh world congress on medical informatics (MEDINFO '92). Geneva, Switzerland; 1992. p. 1462-8.
27. Banerjee S, Pedersen T. An adapted Lesk algorithm for word sense disambiguation using WordNet. In: Proceedings of the third international conference on intelligent text processing and computational linguistics. Mexico City, Mexico; 2002. p. 136-45.
28. Stevenson M, Greenwood M. A semantic approach to IE pattern induction. In: Proceedings of the 43rd annual meeting of the association for computational linguistics. Ann Arbor, MI; 2005. p. 379-86.
29. Raina R, Ng A, Manning C. Robust textual inference via learning and abductive reasoning. In: Proceedings of the Twentieth national conference on artificial intelligence. Pittsburgh, PA; 2005. p. 1099-105.
30. Burgun A, Bodenreider O. Comparing terms, concepts and semantic classes in WordNet and the Unified Medical Language System. In: Proceedings of the NAACL 2001 Workshop: WordNet and other lexical resources: Applications, extensions and customizations. Pittsburgh, PA; 2001. p. 77-82.
31. SNOMED-CT: Fact sheet. 2004. College of American Pathologists.
32. Pakhomov S. Modeling filled pauses in medical dictations. In: Proceedings of the 37th annual meeting of the association for computational linguistics. College Park, MD; 1999. p. 619-24.
33. Carnine D., Kameenui E., and Coyle G. Utilization of contextual information in determining the meaning of unfamiliar words. Read Res Quart 19 (1984) 188-204
34. McDonald S, Ramscar M. Testing the distributional hypothesis: the influence of context on judgements of semantic similarity. In: Proceedings of the 23rd annual conference of the cognitive science society. Edinburgh, Scotland; 2001. p. 611-6.
35. Wu Z, Palmer M. Verb semantics and lexical selection. In: Proceedings of the 32nd annual meeting of the association for computational linguistics. Las Cruces, NM; 1994. p. 133-8.
36. Leacock C., and Chodorow M. Combining local context and WordNet similarity for word sense identification. In: Fellbaum C. (Ed). WordNet: An electronic lexical database (1998), MIT Press, Cambridge, MA 265-283
37. Hirst G., and St-Onge D. Lexical chains as representations of context for the detection and correction of malapropisms. In: Fellbaum C. (Ed). WordNet: An electronic lexical database (1998), MIT Press, Cambridge, MA 305-321
38. Patwardhan, S. Incorporating dictionary and corpus information into a context vector measure of semantic relatedness. Master of Science Thesis, Duluth, MN: Department of Computer Science. Duluth: University of Minnesota; 2003.