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2017 | OriginalPaper | Buchkapitel

WordNet Gloss for Semantic Concept Relatedness

verfasst von : Moch Arif Bijaksana, Rakhmad Indra Permadi

Erschienen in: Recent Advances on Soft Computing and Data Mining

Verlag: Springer International Publishing

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Abstract

Semantic lexical similarity and relatedness are important issues in natural language processing (NLP). Similarity and relatedness are not the same, while they are very closely related. To date, in many works these two issues are mixed up which harm system’s effectiveness. A popular approach to measure semantic similarity and relatedness is utilizing WordNet, a lexical database. This paper shows that Wordnet’s gloss is a potential source for measuring semantic relatedness. Experiment result using WordSim353 relatedness database confirms the effectiveness of the approach.

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A list of WordNet 3.0 glosses in logical forms with XML forms is available in http://wordnetcode.princeton.edu/standoff-files/wn30-lfs.zip.

Agirre, E., Rigau, G.: Word sense disambiguation using conceptual density. In: Proceedings of the 16th Conference on Computational Linguistics (COLING), pp. 16–22. Association for Computational Linguistics (1996)

Agirre, E., Alfonseca, E., Hall, K., Kravalova, J., Paşca, M., Soroa, A.: A study on similarity, relatedness using distributional, wordnet-based approaches. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 19–27. Association for Computational Linguistics (2009)

Bhattacharya, A., Bhowmick, A., Singh, A.K.: Finding Top-k similar pairs of objects annotated with terms from an ontology. In: Gertz, M., Ludäscher, B. (eds.) SSDBM 2010. LNCS, vol. 6187, pp. 214–232. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13818-8_17CrossRef

Caraballo, S.A.: Automatic construction of a hypernym-labeled noun hierarchy from text. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, pp. 120–126. Association for Computational Linguistics (1999)

Dzikovska, M.O., Nielsen, R.D., Brew, C., Leacock, C., Giampiccolo, D., Bentivogli, L., Clark, P., Dagan, I., Dang, H.T.: Semeval-2013 task 7: the joint student response analysis and 8th recognizing textual entailment challenge. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2013), pp. 263–274. Association for Computational Linguistics (2013)

Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., Ruppin, E.: Placing search in context: the concept revisited. ACM Trans. Inf. Syst. 20(1), 116–131 (2002)CrossRef

Gad, W.K., Kamel, M.S.: New semantic similarity based model for text clustering using extended gloss overlaps. In: Perner, P. (ed.) MLDM 2009. LNCS (LNAI), vol. 5632, pp. 663–677. Springer, Heidelberg (2009). doi:10.1007/978-3-642-03070-3_50CrossRef

Gurevych, I.: Using the structure of a conceptual network in computing semantic relatedness. In: Dale, R., Wong, K.-F., Su, J., Kwong, O.Y. (eds.) IJCNLP 2005. LNCS (LNAI), vol. 3651, pp. 767–778. Springer, Heidelberg (2005). doi:10.1007/11562214_67CrossRef

Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th Conference on Computational Linguistics (COLING), pp. 539–545. Association for Computational Linguistics (1992)

10.

Hirst, G., Budanitsky, A.: Correcting real-word spelling errors by restoring lexical cohesion. Nat. Lang. Eng. 11(01), 87–111 (2005)CrossRef

11.

Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of International Conference Research on Computational Linguistics (ROCLING X) (1997)

12.

Khoo, C.S.G., Na, J.-C.: Semantic relations in information science. Ann. Rev. Inf. Sci. Technol. 40, 157 (2006)CrossRef

13.

Leacock, C., Chodorow, M.: Combining local context, WordNet similarity for word sense identification. In: Fellbaum, C., (ed.) WordNet: An Electronic Lexical Database, pp. 265–283 (1998)

14.

Lee, J.H., Kim, M.H., Lee, Y.J.: Information retrieval based on conceptual distance in IS-A hierarchies. J. Documentation 49(2), 188–207 (1993)CrossRef

15.

Lin, D.: Using syntactic dependency as local context to resolve word sense ambiguity. In: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics (ACL-EACL), pp. 64–71. Association for Computational Linguistics (1997)

16.

Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 5th International Conference on Machine Learning, (ICML ’98), vol. 98, pp. 296–304 (1998)

17.

Manabu, O., Takeo, H.: Word sense disambiguation and text segmentation based on lexical cohesion. In: Proceedings of the 15th Conference on Computational Linguistics (COLING), pp. 755–761. Association for Computational Linguistics (1994)

18.

Màrquez, L., Glass, J., Magdy, W., Moschitti, A., Nakov, P., Randeree, B.: Semeval-2015 task 3: Answer selection in community question answering. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015) (2015)

19.

Meij, E., IJzereef, L., Azzopardi, L., Kamps, J., de Rijke, M.: Combining thesauri-based methods for biomedical retrieval. In: Proceeding of The Fourteenth Text REtrieval Conference (TREC) (2005)

20.

Meng, L., Junzhong, G., Zhou, Z.: A new model of information content based on concept’s topology for measuring semantic similarity in wordnet. Intl. J. Grid Distrib. Comput. 5(3), 81–94 (2012)

21.

Miller, G.A.: WordNet: a lexical database for English. Commun. ACM (CACM) 38(11), 39–41 (1995)

22.

Myaeng, S.H., Khoo, C., Li, M.: Linguistic processing of text for a large-scale conceptual information retrieval system. In: Tepfenhart, W.M., Dick, J.P., Sowa, J.F. (eds.) ICCS-ConceptStruct 1994. LNCS, vol. 835, pp. 69–83. Springer, Heidelberg (1994). doi:10.1007/3-540-58328-9_5CrossRef

23.

Pesquita, C., Faria, D., Falcão, A.O., Lord, P., Couto, F.M.: Semantic similarity in biomedical ontologies. PLoS Comput. Biol. 5(7), e1000443 (2009)

24.

Potthast, M., Hagen, M., Beyer, A., Busse, M., Tippmann, M., Rosso, P., Stein, B.: Overview of the 6th international competition on plagiarism detection. In: Cappellato, L., Ferro, N., Halvey, M., Kraaij, W. (eds.) Working Notes Papers of the CLEF 2014 Evaluation Labs, CEUR Workshop Proceedings, CLEF and CEUR-WS.org, September 2014. http://www.clef-initiative.eu/publication/working-notes

25.

Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Trans. Syst. Man Cybern. 19(1), 17–30 (1989)CrossRef

26.

Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI ’95), pp. 448–453 (1995)

27.

Sánchez, D., Batet, M.: A new model to compute the information content of concepts from taxonomic knowledge. Intl. J. Semant. Web Inf. Syst. (IJSWIS) 8(2), 34–50 (2012)CrossRef

28.

Vede, C.: Understanding semantic relationships. VLDB J. 2(4), 455–488 (1993)CrossRef

29.

Sussna, M.: Word sense disambiguation for free-text indexing using a massive semantic network. In: Proceedings of the Second International Conference on Information and Knowledge Management (CIKM ’93), pp. 67–74. ACM (1993)

30.

Voorhees, E.M.: Query expansion using lexical-semantic relations. In: Proceeding of The Seventeenth Annual International ACM/SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 61–69. Springer, London (1994)

31.

Wang, T., Hirst, G.: Refining the notions of depth and density in wordnet-based semantic similarity measures. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1003–1011. Association for Computational Linguistics (2011)

32.

Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, pp. 133–138. Association for Computational Linguistics (1994)

33.

Zhang, Z., Gentile, A.L., Ciravegna, F.: Recent advances in methods of lexical semantic relatedness-a survey. Nat. Lang. Eng. 19(04), 411–479 (2013)CrossRef

Titel: WordNet Gloss for Semantic Concept Relatedness
verfasst von: Moch Arif Bijaksana
Rakhmad Indra Permadi
Verlag: Springer International Publishing
Buch: Recent Advances on Soft Computing and Data Mining
Print ISBN: 978-3-319-51279-2

Electronic ISBN: 978-3-319-51281-5

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-51281-5_41

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