Abstract
The computation of relatedness between two fragments of text or two words is a challenging task in many fields. In this study, we propose a novel method for measuring semantic relatedness between word units and between text units using an iterative process, which we refer to as the word-text mutual guidance (WTMG) method. WTMG combines the surface and contextual information when computing word or text relatedness. The iterative process can start in two different ways: calculating relatedness between texts using the initial relatedness of the words, or computing the relatedness between words using the initial relatedness of the texts. This method obtains the final relatedness result after the iterative process reaches convergence. We compared WTMG with previous relatedness computation methods, which showed that obvious improvements were obtained in terms of the correlation with human judgments.
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References
Fellbaum, C.: WordNet. Wiley Online Library (1999)
Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. IJCAI 7, 1606–1611 (2007)
Hassan, S., Mihalcea, R.: Semantic relatedness using salient semantic analysis. In: AAAI (2011)
Jain, V., Singh, M.: Ontology based information retrieval in semantic web: A survey. International Journal of Information Technology & Computer Science 5(10) (2013)
Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. arXiv preprint cmp-lg/9709008 (1997)
Landauer, T.K., Foltz, P.W., Laham, D.: An introduction to latent semantic analysis. Discourse Processes 25(2-3), 259–284 (1998)
Leacock, C., Chodorow, M.: Combining local context and wordnet similarity for word sense identification. WordNet: An Electronic Lexical Database 49(2), 265–283 (1998)
Lin, D.: An information-theoretic definition of similarity. In: ICML, vol. 98, pp. 296–304 (1998)
Mihalcea, R., Corley, C., Strapparava, C.: Corpus-based and knowledge-based measures of text semantic similarity. In: AAAI, vol. 6, pp. 775–780 (2006)
Moreda, P., Llorens, H., Saquete, E., Palomar, M.: Combining semantic information in question answering systems. Information Processing & Management 47(6), 870–885 (2011)
Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. arXiv preprint cmp-lg/9511007 (1995)
Wenyin, L., Quan, X., Feng, M., Qiu, B.: A short text modeling method combining semantic and statistical information. Information Sciences 180(20), 4031–4041 (2010)
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)
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Liu, B., Feng, J., Liu, M., Liu, F., Wang, X., Li, P. (2014). Computing Semantic Relatedness Using a Word-Text Mutual Guidance Model. In: Zong, C., Nie, JY., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2014. Communications in Computer and Information Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45924-9_7
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DOI: https://doi.org/10.1007/978-3-662-45924-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45923-2
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