Abstract
Argument mining aims at extracting structured arguments from texts. Both argument schemes and their structures are studied. A syllogism is a traditional argument scheme and its figures are important in classical logic. Existing research has not yet investigated syllogism figures. Here, we fill this gap by presenting a study on automatic identification of syllogism figures. We prepared a novel dataset of 8.6k syllogisms. We annotated their figures and carried out identification tasks using both supervised and weakly-supervised approaches. Experimental results show that both approaches are adequate.
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References
Stede, M., Schneider, J.: Argumentation mining. Synth. Lect. Hum. Lang. Technol. 11, 1–191 (2018)
Lawrence, J., Reed, C.: Argument mining: a survey. In: Computational Linguistics, pp. 1–55 (2019)
Walton, D., Reed, C., Macagno, F.: Argumentation Schemes. Cambridge University Press, Cambridge (2008)
Feng, V.W., Hirst, G.: Classifying arguments by scheme. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, USA, pp. 987–996. Association for Computational Linguistics (2011)
Musi, E., Ghosh, D., Muresan, S.: Towards feasible guidelines for the annotation of argument schemes. In: Proceedings of the Third Workshop on Argument Mining, pp. 82–93. Association for Computational Linguistics (2016)
Green, N.: Proposed method for annotation of scientific arguments in terms of semantic relations and argument schemes. In: Proceedings of the 5th Workshop on Argument Mining, Brussels, Belgium, pp. 105–110. Association for Computational Linguistics (2018)
Black, E., Hunter, A.: A relevance-theoretic framework for constructing and deconstructing enthymemes. J. Log. Comput. 22, 55–78 (2012)
Rajendran, P., Bollegala, D., Parsons, S.: Contextual stance classification of opinions: A step towards enthymeme reconstruction in online reviews. In: Proceedings of the First Workshop on Argumentation Mining, pp. 31–39. Association for Computational Linguistics (2016)
Razuvayevskaya, O., Teufel, S.: Finding enthymemes in real-world texts: a feasibility study. Argu. Comput. 8, 1–17 (2017)
Hubien, H.: John buridan on the fourth figure of the syllogism. Revue Internationale de Philoso-phie 113(113), 271–285 (1975)
Dickstein, L.: The effect of figure on syllogistic reasoning. Mem. Cognit. 6, 76–83 (1978)
Hosseini, S.A., Modgil, S., Rodrigues, O.: Enthymeme construction in dialogues using shared knowledge. Front. Artif. Intell. Appl. 266, 325–332 (2014)
Lippi, M., Torroni, P.: Argumentation mining: state of the art and emerging trends. ACM Trans. Internet Technol. 16(2), 10:1-10:25 (2016)
Cabrio, E., Villata, S.: Five years of argument mining: a data-driven analysis. In: International Joint Conferences on Artificial Intelligence Organization, IJCAI-18, pp. 5427–5433 (2018)
Lawrence, J., Reed, C., Allen, C., McAlister, S., Ravenscroft, A.: Mining arguments from 19th century philosophical texts using topic based modeling. In: Proceedings of the First Workshop on Argumentation Mining, Baltimore, Maryland, pp. 79–87. Association for Computational Linguistics (2014)
Aharoni, E., et al.: A benchmark dataset for automatic detection of claims and evidence in the context of controversial topics. In: Proceedings of the First Workshop on Argumentation Mining, pp. 64–68 (2014)
Garca-Villalba, M., Saint-Dizier, P.: A framework to extract arguments in opinion texts. Int. J. Cogn. Inf. Nat. Intell. 6, 62–87 (2012)
Rajendran, P., Bollegala, D., Parsons, S.: Is something better than nothing? automatically predicting stance-based arguments using deep learning and small labelled dataset. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics, pp. 28–34 (2018)
Bowman, S.R., Angeli, G., Potts, C., Manning, C.D.: A large annotated corpus for learning natural language inference. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 632–642 (2015)
Fleiss, J.: Measuring nominal scale agreement among many raters. Psychol. Bull. 76, 378 (1971)
Cortes, C., Vapnik, V.: Support vector network. Mach. Learn. 20, 273–297 (1995)
Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm andother neural network architectures. Neural Netw. Off. J. Int. Neural Netw. Soc. 18, 602–610 (2005)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding (2018). CoRR, abs/1810.04805.
Camburu, O.-M., Rocktäschel, T., Lukasiewicz, T., Blunsom, P.: e-SNLI: natural language inference with natural language explanations. Adv. Neural. Inf. Process. Syst. 31, 9539–9549 (2018)
Acknowledgments
This work is funded by the Humanity and Social Science Youth foundation of Ministry of Education (19YJCZH230) and the Fundamental Research Funds for the Central Universities in BLCU (No.17PT05).
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Peng, S., Liu, L., Liu, C., Yu, D. (2021). Exploring Reasoning Schemes: A Dataset for Syllogism Figure Identification. In: Liu, M., Kit, C., Su, Q. (eds) Chinese Lexical Semantics. CLSW 2020. Lecture Notes in Computer Science(), vol 12278. Springer, Cham. https://doi.org/10.1007/978-3-030-81197-6_37
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