Abstract
Discourse structure analysis is an important task in Natural Language Processing (NLP) and it is helpful to many NLP tasks, such as automatic summarization and information extraction. However, there are only a few researches on Chinese macro discourse structure analysis due to the lack of annotated corpora. In this paper, combining structure recognition with nuclearity recognition, we propose a Label Degeneracy Combination Model (LD-CM) to find the solution of structure recognition in the solution space of nuclearity recognition. Experimental results on the Macro Chinese Discourse TreeBank (MCDTB) show that our model improves the accuracy by 1.21%, compared with the baseline system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, Y.: Research on the structure of Chinese text structure and the construction of resources. Soochow University (2015)
Van Dijk, T.A.: Narrative macro-structures. PTL J. Descr. Poet. Theory Lit. 1, 547–568 (1976)
Hernault, H., Prendinger, H., duVerle, D.A., Ishizuka, M.: HILDA: a discourse parser using support vector machine classification. Dialogue Discourse 1(3), 1–33 (2010)
Joty, S., Carenini, G., Ng, R., Mehdad, Y.: Combining intra- and multi-sentential rhetorical parsing for document-level discourse analysis. In: ACL 2013, vol. 1, pp. 486–496. ACL, Sofia (2013)
Feng, V.W., Hirst, G.: A linear-time bottom-up discourse parser with constraints and post-editing. In: ACL 2014, pp. 511–521. ACL, Baltimore (2014)
Li, J., Li, R., Hovy, E.: Recursive deep models for discourse parsing. In: EMNLP 2014, pp. 2061–2069. ACL, Doha (2014)
Li, Q., Li, T., Chang, B.: Discourse parsing with attention-based hierarchical neural networks. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 362–371. ACL, Austin (2016)
Sagae, K.: Analysis of discourse structure with syntactic dependencies and data-driven shift-reduce parsing. In: International Workshop on Parsing Technologies, pp. 81–84. ACL, Paris (2009)
Feng, V.W., Hirst, G.: Text-level discourse parsing with rich linguistic features. In: ACL 2012, vol. 1, pp. 60–68. ACL, Jeju (2012)
Wang, Y., Li, S., Wang, H.: A two-stage parsing method for text-level discourse analysis. In: ACL 2017, vol. 2, pp. 184–188. ACL, Vancouver (2017)
Lv, G., Na, S., Li, R., Wang, Z., Chai, Q.: Frame-based discourse structure modeling and relation recognition for Chinese sentence. J. Chin. Inf. Process. 29(6), 98–109 (2015)
Sporleder, C., Lascarides, A.: Combining hierarchical clustering and machine learning to predict high-level discourse structure. In: Coling 2004, pp. 43–49. ACM, Geneva (2004)
Jiang, F., Chu, X., Sheng, X., Li, P., Zhu, Q.: A macro discourse primary and secondary relation recognition method. J. Chin. Inf. Process. 32(1), 43–50 (2018)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. Comput. Sci. (2013)
Xu, S.: Research and Implementation of Paraphrase Recognition for Question Answering. Harbin Institute of Technology (2009)
Acknowledgments
The authors would like to thank three anonymous reviewers for their comments on this paper. This research was supported by the National Natural Science Foundation of China under Grant Nos. 61772354, 61773276 and 61472265, and was also supported by the Strategic Pioneer Research Projects of Defense Science and Technology under Grant No. 17-ZLXDXX-02-06-02-04.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Jiang, F., Li, P., Chu, X., Zhu, Q., Zhou, G. (2018). Recognizing Macro Chinese Discourse Structure on Label Degeneracy Combination Model. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-99501-4_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99500-7
Online ISBN: 978-3-319-99501-4
eBook Packages: Computer ScienceComputer Science (R0)
