A Survey of Studies on Discourse Structure and Relation
DOI:
https://doi.org/10.62051/64dyv490Keywords:
Natural Language Processing, Discourse Analysis, Rhetorical Structure Theory (RST), The Penn Discourse TreeBank (PDTB).Abstract
Discourse Analysis aims at high-level semantic and structural analysis. Discourse structure analysis and relation recognition are two key tasks in discourse analysis research, while discourse analysis plays an important role in studying the structure and semantic content of texts. This paper firstly introduces the Rhetorical Structure Theory (RST) and the Penn Discourse TreeBank (PDTB) annotation standards and its corresponding resources establishment of each corpus. Then the mainstream models of discourse structure and relation are expounded. In the last part, the opportunities and challenges of discourse analysis are discussed by combining with the mainstream large language model ChatGPT. In addition, the development prospect of discourse analysis is explored by summarizing the related studies.
Downloads
References
Liao Q.Z., Studies in Discourse, Pragmatics and Syntax [J].Language Teaching and Linguistic Studies, 1991(04):16-44.
Mann W C, Thompson S A. Rhetorical structure theory: Toward a functional theory of text organization[J]. Text-interdisciplinary Journal for the Study of Discourse, 1988, 8(3): 243-281.
Liu S.Z., Zhang Z., Rhetorical Structure Theory and the RST Tools [J]. Technology Enhanced Foreign Languages, 2003(04):20-23.
Marcus M, Santorini B, Marcinkiewicz M A. Building a large annotated corpus of English: The Penn Treebank[J]. Computational linguistics, 1993, 19(2): 313-330.
Le M., An Annotation Study of the Rhetorical Structure of Chinese Discourses [J]. Journal of Chinese Information Processing, 2008, (04):19-23+42.
Prasad R, Miltsakaki E, Dinesh N, et al. The penn discourse treebank 2.0 annotation manual[J]. December, 2007, 17: 2007.
Webber B, Prasad R, Lee A, et al. The penn discourse treebank 3.0 annotation manual[J]. Philadelphia, University of Pennsylvania, 2019, 35: 108.
Subba R, Di Eugenio B. Automatic discourse segmentation using neural networks. In: Proceedings of the 11th Workshop on the Semantics and Pragmatics of Dialogue. 2007, 189–190
Fisher S, Roark B. The utility of parse-derived features for automatic discourse segmentation. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. 2007, 488–495
Hernault H, Bollegala D, Ishizuka M.A sequential model for discourse segmentation. In: Proc.of the CICLing 2010.2010.315-326.
Soricut R, Marcu D.Sentence level discourse parsing using syntactic and lexical information. In: Proc.of the NAACL-HLT 2003.2003.149-156.
Hernault H, Prendinger H, du Verle DA, Ishizuka M. HILDA: A discourse parser using support vector machine classification. Dialogue & Discourse, 2010, 1(3): 1–33.
Wang YZ, Li SJ, Wang HF. A two-stage parsing method for text-level discourse analysis. In: Proc. of the 55th Annual Meeting of the Association for Computational Linguistics (ACL). Vancouver: ACL, 2017. 184-188.
Joty S, Carenini G, Ng R T. Codra: A novel discriminative framework for rhetorical analysis. Computational Linguistics, 2015, 41(3): 385-435
Ji Y, Eisenstein J. One vector is not enough: Entity-augmented distributed semantics for discourse relations. Transactions of the Association for Computational Linguistics, 2015, 3: 329-344
Rutherford A T, Demberg V, Xue N. Neural network models for implicit discourse relation classification in english and chinese without surface features. 2016, arXiv preprint arXiv: 1606.01990
Braud C, Denis P. Comparing word representations for implicit discourse relation classification. In: Proceedings of Empirical Methods in Natural Language Processing (EMNLP 2015). 2015
Chen J, Zhang Q, Liu P, Qiu X, Huang X. Implicit discourse relation detection via a deep architecture with gated relevance network. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016, 1726−1735
Lan M, Xu Y, Niu Z Y. Leveraging synthetic discourse data via multi task learning for implicit discourse relation recognition. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2013, 476–485
Liu Y, Li S, Zhang X, Sui Z. Implicit discourse relation classification via multi-task neural networks. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. 2016, 2750−2756
Guo F, He R, Dang J, Wang J. Working memory-driven neural networks with a novel knowledge enhancement paradigm for implicit discourse relation recognition. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 7822−7829
Feng Z.W., Zhang D.K., Rao G.Q., From Turing Test to ChatGPT: Milestones and Implications for Man-Machine Conversation [J]. Chinese Journal of Language Policy and Planning, 2023,8(02): 20-24. DOI:10.19689/j.cnki.cn10-1361/h.20230202.
Downloads
Published
Conference Proceedings Volume
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.