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How Can the Term Compositionality Be Useful for Acquiring Elementary Semantic Relations?

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Advances in Natural Language Processing (GoTAL 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5221))

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Abstract

Acquiring and enriching lexical resources is crucial for various areas of the computational linguistics applications, especially in specialized domains. In this paper, we propose a high-quality method exploiting the compositionality of complex terms issued from a structured terminology in order to infer three kinds of semantic relations (synonymy, hierarchical and meronymy) between words or terms. The approach has been applied and evaluated on the Gene Ontology biomedical terminology: 1,273 is-a, 178 part-of and 921 synonymy relations have been inferred and show a precision over 90%. We analyze these results and the possibility of their cross-validation through a graph representation.

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References

  1. Burnage, G.: CELEX - A Guide for Users. Centre for Lexical Information, University of Nijmegen (1990)

    Google Scholar 

  2. Hathout, N., Namer, F., Dal, G.: An experimental constructional database: the MorTAL project. In: Boucher, P. (ed.) Morphology book. Cascadilla Press, Cambridge (2001)

    Google Scholar 

  3. N.L.M.: UMLS Knowledge Sources Manual. National Library of Medicine, Bethesda, Maryland (2007), http://www.nlm.nih.gov/research/umls/

  4. Schulz, S., Romacker, M., Franz, P., Zaiss, A., Klar, R., Hahn, U.: Towards a multilingual morpheme thesaurus for medical free-text retrieval. In: Medical Informatics in Europe (MIE) (1999)

    Google Scholar 

  5. Zweigenbaum, P., Baud, R., Burgun, A., Namer, F., Jarrousse, E., Grabar, N., Ruch, P., Duff, F.L., Thirion, B., Darmoni, S.: Towards a Unified Medical Lexicon for French. In: Medical Informatics in Europe (MIE) (2003)

    Google Scholar 

  6. Fellbaum, C.: A semantic network of english: the mother of all WordNets. Computers and Humanities. EuroWordNet: a multilingual database with lexical semantic network 32(2-3), 209–220 (1998)

    Article  Google Scholar 

  7. Smith, B., Fellbaum, C.: Medical wordnet: a new methodology for the construction and validation of information. In: Proc of 20th CoLing, Geneva, Switzerland, pp. 371–382 (2004)

    Google Scholar 

  8. Resnik, P.: Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research (JAIR) 11, 95–130 (1999)

    MATH  Google Scholar 

  9. Bousquet, C., Jaulent, M.C., Chatellier, G., Degoulet, P.: Using semantic distance for the efficient coding of medical concepts. In: Annual Symposium of the American Medical Informatics Association (AMIA), Los Angeles, CA, pp. 96–100 (2000)

    Google Scholar 

  10. Lord, P., Stevens, R., Brass, A., Goble, C.: Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation. Bioinformatics 19(10), 1275–1283 (2003)

    Article  Google Scholar 

  11. Gene Ontology Consortium: Creating the Gene Ontology resource: design and implementation. Genome Research 11, 1425–1433 (2001)

    Google Scholar 

  12. Partee, B.H.: In: Landman, F., Veltman, F. (eds.) Compositionality (1984)

    Google Scholar 

  13. Hamon, T., Nazarenko, A., Poibeau, T., Aubin, S., Derivière, J.: A robust linguistic platform for efficient and domain specific web content analysis. In: RIAO 2007, Pittsburgh, USA (2007)

    Google Scholar 

  14. Berroyer, J.F.: Tagen, un analyseur d”entits nommes: conception, development et valuation. In: Mmoire de D.E.A. d’intelligence artificielle, UniversitParis-Nord (2004)

    Google Scholar 

  15. Tsuruoka, Y., Tateishi, Y., Kim, J.D., Ohta, T., McNaught, J., Ananiadou, S., Tsujii, J.: Developing a robust part-of-speech tagger for biomedical text. In: Bozanis, P., Houstis, E.N. (eds.) PCI 2005. LNCS, vol. 3746, pp. 382–392. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  16. Aubin, S., Hamon, T.: Improving term extraction with terminological resources. In: Salakoski, T., Ginter, F., Pyysalo, S., Pahikkala, T. (eds.) FinTAL 2006. LNCS (LNAI), vol. 4139, pp. 380–387. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  17. Hamon, T., Nazarenko, A., Gros, C.: A step towards the detection of semantic variants of terms in technical documents. In: International Conference on Computational Linguistics (COLING-ACL 1998), Université de Montréal, Montréal, Quebec, Canada, pp. 498–504 (1998)

    Google Scholar 

  18. Verspoor, C.M., Joslyn, C., Papcun, G.J.: The gene ontology as a source of lexical semantic knowledge for a biological natural language processing application. In: SIGIR workshop on Text Analysis and Search for Bioinformatics, pp. 51–56 (2003)

    Google Scholar 

  19. National Library of Medicine Bethesda, Maryland: Medical Subject Headings (2001), http://www.nlm.nih.gov/mesh/meshhome.html

  20. Côté, R.A.: Répertoire d’anatomopathologie de la SNOMED internationale, v.3.4. Université de Sherbrooke, Sherbrooke, Québec (1996)

    Google Scholar 

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Hamon, T., Grabar, N. (2008). How Can the Term Compositionality Be Useful for Acquiring Elementary Semantic Relations?. In: Nordström, B., Ranta, A. (eds) Advances in Natural Language Processing. GoTAL 2008. Lecture Notes in Computer Science(), vol 5221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85287-2_18

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  • DOI: https://doi.org/10.1007/978-3-540-85287-2_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85286-5

  • Online ISBN: 978-3-540-85287-2

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