Identyfikatory
Warianty tytułu
Konferencja
Federated Conference on Computer Science and Information Systems (09-12.09.2018 ; Poznań, Poland)
Języki publikacji
Abstrakty
Recognizing textual entailment (RTE) became a well established and widely studied task. Partial textual entailment -- and faceted textual entailment in particular -- belong to tasks that are derived from RTE. Although there exist many annotated corpora for the original RTE problem, faceted textual entailment is in the sense of easy-accessible corpora highly neglected. In this paper, we present a semi-automatic approach to deriving corpora for faceted entailment task from a general RTE corpus using open information extraction (open IE) tools.
Rocznik
Tom
Strony
33--38
Opis fizyczny
Bibliogr. 23 poz.
Twórcy
autor
- NLP Centre, Faculty of Informatics, Masaryk University, Botanická 68a, 602 00 Brno, Czech Republic
Bibliografia
- [1] R. D. Nielsen, W. Ward, and J. H. Martin, “Recognizing entailment in intelligent tutoring systems,” Natural Language Engineering, vol. 15, no. 4, pp. 479–501, 2009. doi: 10.1017/S135132490999012X
- [2] M. Dzikovska, R. D. Nielsen, C. Brew, C. Leacock, D. Giampiccolo, L. Bentivogli, P. Clark, I. Dagan, and H. T. Dang, “Semeval-2013 task 7: The joint student response analysis and 8th recognizing textual entailment challenge,” NORTH TEXAS STATE UNIV DENTON, Tech. Rep., 2013.
- [3] R. Ghaeini, S. A. Hasan, V. Datla, J. Liu, K. Lee, A. Qadir, Y. Ling, A. Prakash, X. Z. Fern, and O. Farri, “Dr-bilstm: Dependent reading bidirectional lstm for natural language inference,” arXiv preprint arXiv:1802.05577, 2018.
- [4] M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, “Deep contextualized word representations,” arXiv preprint arXiv:1802.05365, 2018.
- [5] Y. Tay, L. A. Tuan, and S. C. Hui, “A compare-propagate architecture with alignment factorization for natural language inference,” arXiv preprint arXiv:1801.00102, 2017.
- [6] R. Zanoli and S. Colombo, “A transformation-driven approach for recognizing textual entailment,” Natural Language Engineering, vol. 23, no. 4, pp. 507–534, 2017. doi: 10.1017/S1351324916000176
- [7] I. Dagan, O. Glickman, and B. Magnini, “The pascal recognising textual entailment challenge,” in Machine learning challenges. evaluating predictive uncertainty, visual object classification, and recognising tectual entailment. Springer, 2006, pp. 177–190.
- [8] D. Z. Korman, E. Mack, J. Jett, and A. H. Renear, “Defining textual entailment,” Journal of the Association for Information Science and Technology, vol. 69, no. 6, pp. 763–772, 2018. doi: 10.1002/asi.24007
- [9] L. Bentivogli, I. Dagan, and B. Magnini, “The recognizing textual entailment challenges: Datasets and methodologies,” in Handbook of Linguistic Annotation. Springer, 2017, pp. 1119–1147.
- [10] S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning, “A large annotated corpus for learning natural language inference,” in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, 2015.
- [11] G. Rocha and H. Lopes Cardoso, “Recognizing textual entailment: Challenges in the portuguese language,” Information, vol. 9, no. 4, p. 76, 2018. doi: 10.3390/info9040076
- [12] M.-S. Guo, Y. Zhang, and T. Liu, “Research advances and prospect of recognizing textual entailment and knowledge acquisition,” Jisuanji Xuebao/Chinese Journal of Computers, vol. 40, pp. 889–910, 04 2017. doi: 10.11897/SP.J.1016.2017.00889
- [13] M. Banko, M. J. Cafarella, S. Soderland, M. Broadhead, and O. Etzioni, “Open information extraction from the web.” in IJCAI, vol. 7, 2007, pp. 2670–2676.
- [14] F. Wu and D. S. Weld, “Open information extraction using wikipedia,” in Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2010, pp. 118–127.
- [15] M. Mausam, “Open information extraction systems and downstream applications,” in Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. AAAI Press, 2016, pp. 4074–4077.
- [16] A. Fader, S. Soderland, and O. Etzioni, “Identifying relations for open information extraction,” in Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, 2011, pp. 1535–1545.
- [17] L. Del Corro and R. Gemulla, “Clausie: clause-based open information extraction,” in Proceedings of the 22nd international conference on World Wide Web. ACM, 2013, pp. 355–366.
- [18] G. Angeli, M. J. J. Premkumar, and C. D. Manning, “Leveraging linguistic structure for open domain information extraction,” in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, 2015, pp. 344–354.
- [19] M. Víta and V. Kríž, “Word2vec based system for recognizing partial textual entailment,” in Computer Science and Information Systems (FedCSIS), 2016 Federated Conference on. IEEE, 2016, pp. 513–516.
- [20] G. Stanovsky and I. Dagan, “Creating a large benchmark for open information extraction,” in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016, pp. 2300–2305.
- [21] O. Levy, T. Zesch, I. Dagan, and I. Gurevych, “Recognizing partial textual entailment,” in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, 2013, pp. 451–455.
- [22] P. Groth, M. Lauruhn, A. Scerri, and R. Daniel Jr, “Open information extraction on scientific text: An evaluation,” arXiv preprint arXiv:1802.05574, 2018.
- [23] P. Brazdil and C. Giraud-Carrier, “Metalearning and algorithm selection: progress, state of the art and introduction to the 2018 special issue,” 2018.
Uwagi
1. Track 1: Computer Science & Systems
2. Technical Session: 3rd International Workshop on Language Technologies and Applications
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-920d7937-c5fe-4019-a056-35ff7faaab2f