Abstract
Word sense disambiguation (WSD) is the task of determining the meaning of an ambiguous word. It is an open problem in natural language processing because effective WSD can improve the quality of related fields such as information retrieval. Although WSD systems achieve sufficiently high levels of accuracy thanks to several technologies, it remains a challenging problem in the medical domain. In this paper, we propose a conceptual model to resolve the word sens ambiguity problem using the semantic relations between extracted concepts, through MetaMap tool and UMLS Metathesaurus. The evaluation of our disambiguation model is done through the use of information retrieval domain. Results carried out with Clef medical image retrieval 2009 show that our WSD model improves the results that are obtained by the MetaMap WSD model.
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References
Agirre, E., Rigau, G.: Word sense disambiguation using conceptual density. In: Proceedings of the 16th Conference on Computational Linguistics, COLING 1996, vol. 1, pp. 16–22. Association for Computational Linguistics, Stroudsburg (1996)
Agirre, E., Soroa, A., Stevenson, M.: Graph-based word sense disambiguation of biomedical documents. Bioinformatics 26(22), 2889–2896 (2010)
Andreopoulos, B., Alexopoulou, D., Schroeder, M.: Word sense disambiguation in biomedical ontologies with term co-occurrence analysis and document clustering. Int. J. Data Min. Bioinformatics 2(3), 193–215 (2008)
Aronson, A.R.: Metamap: Mapping text to the umls metathesaurus (1996), http://ii.nlm.nih.gov/resources/metamap.pdf
Aronson, A.R.: Effective mapping of biomedical text to the umls metathesaurus: the metamap program. In: Proc. AMIA Symp., pp. 17–21 (2001)
Boyd-Graber, J., Blei, D., Zhu, X.: A Topic Model for Word Sense Disambiguation (2007)
Chen, P., Al-Mubaid, H.: Context-based term disambiguation in biomedical literature. In: Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2006, pp. 62–67 (2006)
Chevallet, J., Lim, J., Le, D.T.H.: Domain knowledge conceptual inter-media indexing: application to multilingual multimedia medical reports. In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, CIKM 2007, pp. 495–504. ACM, New York (2007)
Cimino, J.J., Min, H., Perl, Y.: Consistency across the hierarchies of the umls semantic network and metathesaurus. Journal of Biomedical Informatics 36(6), 450–461 (2003)
Hwang, M., Choi, C., Kim, P.: Automatic enrichment of semantic relation network and its application to word sense disambiguation. IEEE Trans. Knowl. Data Eng. 23(6), 845–858 (2011)
Jimeno-Yepes, A.J., McInnes, B.T., Aronson, A.R.: Exploiting mesh indexing in medline to generate a data set for word sense disambiguation. BMC Bioinformatics 12, 223 (2011)
Joshi, M., Pedersen, T., Maclin, R.: A comparative study of support vector machines applied to the supervised word sense disambiguation problem in the medical domain. In: Proceedings of the 2nd Indian International Conference on Artificial Intelligence, IICAI 2005, pp. 3449–3468 (2005)
Li, L., Roth, B., Sporleder, C.: Topic models for word sense disambiguation and token-based idiom detection. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, ACL 2010, pp. 1138–1147. Association for Computational Linguistics, Stroudsburg (2010)
Magnini, B., Pezzulo, G., Gliozzo, A.: Using domain information for word sense disambiguation. In: Proc. of SENSEVAL2 (2001)
Plaza, L., Stevenson, M., DÃaz, A.: Resolving ambiguity in biomedical text to improve summarization. Inf. Process. Manage. 48(4), 755–766 (2012)
Salton, G.: The smart retrieval system: Experiments in automatic document processing (1970)
Salton, G., McGill, M.: Introduction to modern information retrieval (1983)
Ventresque, A., Cazalens, S., Lamarre, P., Valduriez, P.: Improving interoperability using query interpretation in semantic vector spaces. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 539–553. Springer, Heidelberg (2008)
Weeber, M., Mork, J., Aronson, A.: Developing a test collection for biomedical word sense disambiguation. In: Proceedings of AMIA Annual Symposium, pp. 746–750 (2001)
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Gasmi, K., Torjmen Khemakhem, M., Ben Jemaa, M. (2013). A Conceptual Model for Word Sense Disambiguation in Medical Image Retrieval. In: Banchs, R.E., Silvestri, F., Liu, TY., Zhang, M., Gao, S., Lang, J. (eds) Information Retrieval Technology. AIRS 2013. Lecture Notes in Computer Science, vol 8281. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45068-6_26
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DOI: https://doi.org/10.1007/978-3-642-45068-6_26
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