Abstract
Question-answering systems face a challenge related to the process of deciding automatically about the veracity of a given answer. This issue is particularly problematic when handling open-ended questions. In this paper, we propose a multilingual semantic similarity-based approach to estimate the similarity score between the user’s answer and the right one saved in the data tier. This approach is mainly based on semantic information notably the synonymy relationships between words and syntactico-semantic information especially semantic class and thematic role. It supports three languages: English, French and Arabic. Our approach is applied to a multilingual ontology-based question-answering training for Alzheimer’s disease patients. The performance of the pro- posed approach was confirmed through experiments on 20 patients that promising capabilities in identifying literal and some types of intelligent similarity.
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References
Agirre, E., Cer, D., Diab, M., Gonzalez-Agirre, A., Guo, W.: SEM 2013 shared task: semantic textual similarity, including a pilot on typed-similarity. In: In* SEM 2013: The Second Joint Conference on Lexical and Computational Semantics. Association for Computational Linguistics. Citeseer (2013)
Anjaneyulu, M., Sarma, S.S.V.N., Vijaya Pal Reddy, P., Prem Chander, K., Nagaprasad, S.: Sentence similarity using syntactic and semantic features for multi-document summarization. In: Bhattacharyya, S., Hassanien, A.E., Gupta, D., Khanna, A., Pan, I. (eds.) International Conference on Innovative Computing and Communications. LNNS, vol. 56, pp. 471–485. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-2354-6_49
Course, L.: Lexical semantics of verbs vi: assessing semantic determinants of argument realization, July 2009
Ferreira, R., Lins, R.D., Simske, S.J., Freitas, F., Riss, M.: Assessing sentence similarity through lexical, syntactic and semantic analysis. Comput. Speech Lang. 39, 1–28 (2016)
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). https://doi.org/10.1007/978-3-642-03070-3_50
Herradi, N., Hamdi, F., Métais, E., Ghorbel, F., Soukane, A.: PersonLink: an ontology representing family relationships for the CAPTAIN MEMO memory prosthesis. In: Jeusfeld, M.A., Karlapalem, K. (eds.) ER 2015. LNCS, vol. 9382, pp. 3–13. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25747-1_1
Islam, A., Inkpen, D.: Semantic text similarity using corpus-based word similarity and string similarity. ACM Trans. Knowl. Discov. Data (TKDD) 2(2), 10 (2008)
Jaccard, P.: Etude comparative de la distribution florale dans une portion des Alpes et du Jura. Impr. Corbaz (1901)
Khemakhem, A., Gargouri, B., Hamadou, A.B., Francopoulo, G.: ISO standard modeling of a large arabic dictionary. Nat. Lang. Eng. 22, 1–31 (2015)
Lehmann, J., et al.: DBpedia - a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web J. 6(2), 167–195 (2015). http://jens-lehmann.org/files/2015/swjdbpedia.pdf
Li, Y., Mclean, D., Bandar, Z., O’Shea, J., Crockett, K.: Sentence similarity based on semantic nets and corpus statistics. IEEE Trans. Knowl. Data Eng. 18(8), 1138–1150 (2006). https://doi.org/10.1109/TKDE.2006.130
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: Association for Computational Linguistics (ACL) System Demonstrations, pp. 55–60 (2014). http://www.aclweb.org/anthology/P/P14/P14-5010
Metais, E., et al.: Memory prosthesis. Non-Pharmacol. Ther. Dement. 3(2), 177–180 (2015). ISSN 1949–484X
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995). https://doi.org/10.1145/219717.219748
Oliva, J., Serrano, J.I., del Castillo, M.D., Iglesias, Á.: SyMSS: a syntax-based measure for short-text semantic similarity. Data Knowl. Eng. 70(4), 390–405 (2011)
Schuler, K.K.: VerbNet: a broad-coverage, comprehensive verb lexicon. Ph.D. thesis, University of Pennsylvania (2006). http://verbs.colorado.edu/~kipper/Papers/dissertation.pdf
Silberztein, M., Váradi, T., Tadić, M.: Open source multi-platform NooJ for NLP. In: Proceedings of COLING 2012: Demonstration Papers, pp. 401–408. The COLING 2012 Organizing Committee, Mumbai, December 2012. https://www.aclweb.org/anthology/C12-3050
Sultan, M.A., Bethard, S., Sumner, T.: Dls@ cu: sentence similarity from word alignment. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 241–246 (2014)
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Wali, W., Ghorbel, F., Gragouri, B., Hamdi, F., Metais, E. (2019). A Multilingual Semantic Similarity-Based Approach for Question-Answering Systems. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_54
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