Evaluating measures of semantic similarity and relatedness to disambiguate terms in biomedical text

Journal of Biomedical Informatics

Volumn 46, Issue 6, 2013, Pages 1116-1124

  McInnes, Bridget T ^a   Pedersen, Ted ^b  

^a UNIVERSITY OF MINNESOTA   (United States)

^b UNIVERSITY OF MINNESOTA DULUTH   (United States)

Author keywords

Biomedical documents; Natural language processing; NLP; Semantic similarity and relatedness; Word sense disambiguation; WSD

Indexed keywords

BIOMEDICAL DOCUMENTS; NATURAL LANGUAGE PROCESSING; NLP; SEMANTIC SIMILARITY; WORD SENSE DISAMBIGUATION; WSD;

KNOWLEDGE BASED SYSTEMS; OPEN SYSTEMS;

NATURAL LANGUAGE PROCESSING SYSTEMS;

ACCURACY; ALGORITHM; ARTICLE; EVALUATION; INFORMATION PROCESSING; MEASUREMENT; MEDLINE; OXFORD MEDICAL INFORMATION SYSTEM; PRIORITY JOURNAL; PROCESS DEVELOPMENT; SEMANTICS; TAXONOMY; UNIFIED MEDICAL LANGUAGE SYSTEM; WORD RECOGNITION;

BIOMEDICAL DOCUMENTS; NATURAL LANGUAGE PROCESSING; NLP; SEMANTIC SIMILARITY AND RELATEDNESS; WORD SENSE DISAMBIGUATION; WSD;

EVALUATION STUDIES AS TOPIC; SEMANTICS; UNIFIED MEDICAL LANGUAGE SYSTEM;

EID: 84888198960     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2013.08.008     Document Type: Article

References (34)

1. 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; 2003. p. 241-57.
2. McInnes B, Pedersen T, Liu Y, Pakhomov S, Melton G. Knowledge-based method for determining the meaning of ambiguous biomedical terms using information content measures of similarity. In: Proceedings of the American medical informatics association symposium, Washington, DC; 2011.
3. Garla V.N., Brandt C. Knowledge-based biomedical word sense disambiguation: an evaluation and application to clinical document classification. J Am Med Inform Assoc 2012, 0(5):1-5.
4. Zhong Z, Ng H. It makes sense: a wide-coverage word sense disambiguation system for free text. In: Proceedings of the ACL 2010 system demonstrations, association for computational linguistics; 2010. p. 78-83.
5. Stevenson M., Guo Y., Gaizauskas R., Martinez D. Disambiguation of biomedical text using diverse sources of information. BMC Bioinformatics 2008, 9(Suppl. 11):11.
6. Brody S, Lapata M. Bayesian word sense induction. In: Proceedings of the 12th conference of the European chapter of the association for computational linguistics; 2009. p. 103-11.
7. Pedersen T. The effect of different context representations on word sense discrimination in biomedical texts. In: Proceedings of the 1st ACM international health informatics symposium; 2010. p. 56-65.
8. Navigli R., Faralli S., Soroa A., de Lacalle O., Agirre E. Two birds with one stone: learning semantic models for text categorization and word sense disambiguation. Proceedings of the 20th ACM international conference on Information and knowledge management 2011, 2317-2320. ACM.
9. Humphrey S., Rogers W., Kilicoglu H., Demner-Fushman D., Rindflesch T. Word sense disambiguation by selecting the best semantic type based on journal descriptor indexing: preliminary experiment. J Am Soc Inform Sci Technol 2006, 57(1):96-113. doi:http://dx.doi.org/10.1002/asi.v57:1.
10. Alexopoulou D., Andreopoulos B., Dietze H., Doms A., Gandon F., Hakenberg J., et al. Biomedical word sense disambiguation with ontologies and metadata: automation meets accuracy. BMC Bioinformatics 2009, 10(1):28.
11. Jimeno-Yepes A., Aronson A. Knowledge-based biomedical word sense disambiguation: comparison of approaches. BMC Bioinformatics 2010, 11(1):569.
12. Lesk M. Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In: Proceedings of the 5th annual international conference on systems documentation; 1986. p. 24-6.
13. Jimeno-Yepes A., McInnes B., Aronson A. An unsupervised vector approach to biomedical term disambiguation: integrating umls and medline. BMC Bioinform 2011, 12(1):223.
14. McInnes B.T. An unsupervised vector approach to biomedical term disambiguation: integrating umls and medline. Proceedings of the 46th annual meeting of the association for computational linguistics on human language technologies: student research workshop 2008, 49-54. Association for Computational Linguistics.
15. Agirre E., Soroa A., Stevenson M. Graph-based word sense disambiguation of biomedical documents. Bioinformatics 2010, 26(22):2889-2896.
16. Stevenson M., Agirre E., Soroa A. Exploiting domain information for word sense disambiguation of medical documents. J Am Med Inform Assoc 2012, 19(2):235-240.
17. Rada R., Mili H., Bicknell E., Blettner M. Development and application of a metric on semantic nets. IEEE Trans Syst Man Cybern 1989, 19(1):17-30.
18. Caviedes J., Cimino J. Towards the development of a conceptual distance metric for the umls. J Biomed Inform 2004, 37(2):77-85.
19. Wu Z, Palmer M. Verbs semantics and lexical selection. In: Proceedings of the 32nd meeting of association of computational linguistics; 1994. p. 133-8.
20. Leacock C., Chodorow M. Combining local context and WordNet similarity for word sense identification. WordNet: An Electron Lexical Database 1998, 49(2):265-283.
21. Nguyen H, Al-Mubaid H. New ontology-based semantic similarity measure for the biomedical domain. In: Proceedings of the IEEE international conference on granular computing; 2006. p. 623-8.
22. Resnik P. Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th international joint conference on artificial intelligence; 1995. p. 448-53.
23. Jiang J, Conrath D. Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings on international conference on research in computational linguistics; 1997. p. 19-33.
24. Lin D. An information-theoretic definition of similarity. In: Proceedings of the international conference on machine learning; 1998. p. 296-304. . http://citeseer.ist.psu.edu/95071.html.
25. Sánchez D., Batet M., Isern D. Ontology-based information content computation. Knowledge-Based Syst 2011, 24(2):297-303.
26. Banerjee S, Pedersen T. Extended gloss overlaps as a measure of semantic relatedness. In: Proceedings of the 18th international joint conference on AI; 2003. p. 805-10.
27. 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.
28. Liu Y., McInnes B., Pedersen T., Melton-Meaux G., Pakhomov S. Semantic relatedness study using second order co-occurrence vectors computed from biomedical corpora, umls and wordnet. Proceedings of the 2nd ACM SIGHIT symposium on international health informatics 2012, 363-372. ACM.
29. McInnes B, Pedersen T, Pakhomov S. UMLS-interface and UMLS-similarity: open source software for measuring paths and semantic similarity. In: Proceedings of the American medical informatics association symposium, San Fransico, CA; 2009.
30. Ide N., Loane R., Demner-Fushman D. Essie: a concept-based search engine for structured biomedical text. J Am Med Inform Assoc 2007, 14(3):253-263.
31. Zeng Q., Cimino J. Automated knowledge extraction from the umls. Proc AMIA Symp 1998, 568. American Medical Informatics Association.
32. Choueka Y., Lusignan S. Disambiguation by short contexts. Comput Humanit 1985, 19(3):147-157.
33. Weeber M, Mork J, Aronson A. Developing a test collection for biomedical word sense disambiguation. In: Proceedings of the American medical informatics association symposium, Washington, DC; 2001. p. 746-50.
34. Stevenson M, Guo Y, Al Amri A, Gaizauskas R. Disambiguation of biomedical abbreviations. In: Proceedings of the ACL BioNLP workshop; 2009. p. 71-9.