Word2vec Based System for Recognizing Partial Textual Entailment
Martin Víta, Vincent Kríž
Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 513–516 (2016)
Abstract. Recognizing textual entailment is typically considered as a binary decision task -- whether a text $T$ entails a hypothesis $H$. Thus, in case of a negative answer, it is not possible to express that $H$ is ``almost entailed'' by $T$. Partial textual entailment provides one possible approach to this issue. This paper presents an attempt to use word2vec model for recognizing partial (faceted) textual entailment. The proposed approach does not rely on language dependent NLP tools and other linguistic resources, therefore it can be easily implemented in different language environments where word2vec models are available.
- I. Androutsopoulos and P. Malakasiotis, “A survey of paraphrasing and textual entailment methods,” Journal of Artificial Intelligence Research, pp. 135–187, 2010.
- R. D. Nielsen, W. Ward, and J. H. Martin, “Recognizing entailment in intelligent tutoring systems,” Natural Language Engineering, vol. 15, no. 04, pp. 479–501, 2009.
- O. Levy, T. Zesch, I. Dagan, and I. Gurevych, “Recognizing partial textual entailment.” in ACL (2), 2013, pp. 451–455.
- P. Resnik, “Using information content to evaluate semantic similarity in a taxonomy,” in Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI 95, Montréal Québec, Canada, August 20-25 1995, 2 Volumes, 1995, pp. 448–453.
- C. Fellbaum, WordNet. Wiley Online Library, 1998.
- D. R. G. H. R. Williams and G. Hinton, “Learning representations by back-propagating errors,” Nature, pp. 523–533, 1986.
- T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” arXiv preprint https://arxiv.org/abs/1301.3781, 2013.
- M. O. Dzikovska, R. D. Nielsen, and C. Brew, “Towards effective tutorial feedback for explanation questions: A dataset and baselines,” in Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 2012, pp. 200–210.
- T. Mikolov, W.-t. Yih, and G. Zweig, “Linguistic regularities in continuous space word representations.” in HLT-NAACL, 2013, pp. 746–751.
- J. A. Miñarro-Giménez, O. Marı́n-Alonso, and M. Samwald, “Applying deep learning techniques on medical corpora from the world wide web: a prototypical system and evaluation,” arXiv preprint https://arxiv.org/abs/1502.03682, 2015.
- K. Jassem and L. Pawluczuk, “Automatic summarization of polish news articles by sentence selection,” in 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015, Lódz, Poland, September 13-16, 2015, 2015, pp. 337–341. [Online]. Available: http://dx.doi.org/10.15439/2015F186
- T. Mikolov, Q. V. Le, and I. Sutskever, “Exploiting similarities among languages for machine translation,” arXiv preprint https://arxiv.org/abs/1309.4168, 2013.