Abstract
The variability of semantic expression is a special characteristic of natural language. This variability is challenging for many natural language processing applications that try to infer the same meaning from different text variants. In order to treat this problem a generic task has been proposed: Textual Entailment Recognition. In this paper, we present a new Textual Entailment approach based on Latent Semantic Indexing (LSI) and the cosine measure. This proposed approach extracts semantic knowledge from different corpora and resources. Our main purpose is to study how the acquired information can be combined with an already developed and tested Machine Learning Entailment system (MLEnt). The experiments show that the combination of MLEnt, LSI and cosine measure improves the results of the initial approach.
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Kozareva, Z., Montoyo, A.: The role and the resolution of textual entailment for natural language processing applications. In: 11th International Conference on Applications of Natural Language to Information Systems (NLDB) (2006)
Dagan, I., Glickman, O., Magnini, B.: The pascal recognising textual entailment challenge. In: Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment (2005)
Dagan, I., Glickman, O.: Probabilistic textual entailment: Generic applied modeling of language variability. In: PASCAL Workshop on Learning Methods for Text Understanding and Mining (2004)
Akhmatova, E.: Textual entailment resolution via atomic propositions. In: Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment, pp. 61–64 (2005)
Herrera, J., Peñas, A., Verdejo, F.: Textual entailment recognition based on dependency analysis and wordnet. In: Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment (2005)
Jijkoun, V., de Rijke, M.: Recognizing textual entailment using lexical similarity. In: Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment (2005)
Montes, M., Gelbukh, A., López, A., Baeza-Yates, R.: Flexible comparison of conceptual graphs. In: Mayr, H.C., Lazanský, J., Quirchmayr, G., Vogel, P. (eds.) DEXA 2001. LNCS, vol. 2113, pp. 102–111. Springer, Heidelberg (2001)
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic indexing. Journal of the American Society for Information Science 41, 321–407 (1990)
Magnini, B., Cavaglia, G.: Integrating Subject Field Codes into WordNet. In: Gavrilidou, M., Crayannis, G., Markantonatu, S., Piperidis, S., Stainhaouer, G. (eds.) Proceedings of LREC-2000, Second International Conference on Language Resources and Evaluation, Athens, Greece, pp. 1413–1418 (2000)
Vázquez, S., Montoyo, A., Rigau, G.: Using relevant domains resource for word sense disambiguation. In: IC-AI, pp. 784–789 (2004)
Kozareva, Z., Montoyo, A.: Mlent: The machine learning entailment system of the university of alicante. In: Proceedings of the PASCAL Challenges Workshop on Recognising Textual Entailment (2006)
Aston, G.: The british national corpus as a language learner resource. In: TALC 1996 (1996)
Church, K., Hanks, P.: Word association norms, mutual information and lexicograhy. Computational Lingüistics 16, 22–29 (1990)
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Vázquez, S., Kozareva, Z., Montoyo, A. (2006). Textual Entailment Beyond Semantic Similarity Information. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_86
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DOI: https://doi.org/10.1007/11925231_86
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