Abstract
The discourse clause relational semantics is the semantic relation between discourse clause relevance structures. This paper proposes a method to represent the discourse clause relational semantics as a multi-dimensional feature structure. Compared with the simple classification mechanism of discourse relations, it can reveal the discourse semantic relations more deeply. Furthermore, we built Chinese discourse clause relational semantic feature corpus, and study the clause relational semantic feature recognition. We Transfer the clause relational semantic feature recognition into multiple binary classification problems, and extract relevant classification features for experiment. Experiments show that under the best classifier (SVM), the overall semantic feature recognition effect of F1 value reaches 70.14%; each classification feature contributes differently to the recognition of different clause relational semantic features, and the connectives contributes more to the recognition of all semantic features. By adding related semantic features as classification features, the interaction between different semantic features is studied. Experiments show that the influence of different semantic features is different. The addition of multiple semantic features has a more significant effect than a single semantic feature.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Feng, W., Chen, Y., Ren, Y., Ren, H.: Representation and recognition of clauses relevance structure in Chinese text. Acta Scientiarum Naturalium Universitatis Pekinensis 56(1), 23–30 (2020)
Carlson, L., Marcu, D., Okurowski, M.E.: Building a discourse-tagged corpus in the framework of rhetorical structure theory. In: van Kuppevelt, J., Smith, R.W. (eds.) Current and New Directions in Discourse and Dialogue. Text, Speech and Language Technology, vol. 22, pp. 85–112. Springer, Dordrecht (2003). https://doi.org/10.1007/978-94-010-0019-2_5
Prasad, R., Dinesh, N., Lee, A., et al.: The Penn Discourse Treebank 2.0. In: Proceedings of the 6th International Conference on Language Resources and Evaluation (LREC), Marrakech, pp. 2961–2968 (2008)
Li, Y., Feng, W., Kong, F., et al.: Building Chinese discourse corpus with connective-driven dependency tree structure. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, pp. 2105–2114(2014)
Li, S., Wang, L., Cao, Z., Li, W.: Text-level discourse dependency parsing. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL), Baltimore, pp. 25–35 (2014)
Yang, A., Li, S.: SciDTB: discourse dependency TreeBank for scientific abstracts. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL), Melbourne, pp. 444–449 (2018)
The PDTB Research Group: The Penn Discourse Treebank 2.0 Annotation Manual (2007)
Feng, W.: Feature structure for relationship of Chinese complex sentence. J. Chin. Inf. Process. 29(6), 13–21 (2015)
Iruskieta, M., De Ilarraza, A.D., Lersundi, M.: The annotation of the central unit in rhetorical structure trees: a key step in annotating rhetorical relations. In: Proceedings of the 25th International Conference on Computational Linguistics: Technical Papers, pp. 466–475 (2014)
Carlson, L., Marcu, D.: Discourse tagging reference manual. Technical Report ISI-TR-545, University of Southern, California (2001)
Hu, Y.: Modern Chinese, vol. 6. Shanghai Educational Press, Shanhai (1995)
Boutell, M.R., Luo, J., Shen, X., et al.: Learning multi-label scene classification. Pattern Recognit. 37(9), 1757–1771 (2004)
Read, J., Pfahringer, B., Holmes, G., et al.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333–359 (2011)
Read, J., Pfahringer, B., Holmes, G.: Multi-label classification using ensembles of pruned sets. In: Proceedings of ICDM 2008, 8th IEEE International Conference on Data Mining, pp. 995–1000 (2008)
Pitler, E., Louis, A., Nenkova, A.: Automatic sense prediction for implicit discourse relations in text. In: Proceedings of the ACL-IJCNLP 2009. Association for Computational Linguistics, Stroudsburg, pp. 683–691 (2009)
Marcu, D., Echihabi, A.: An unsupervised approach to recognizing discourse relations. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Morristown, pp. 368–375 (2002)
Li, Y., Feng, W., Zhou, G., Zhu, K.: Research of Chinese clause identification based on comma. Acta Scientiarum Naturalium Universitatis Pekinensis 49(1), 7–14 (2013)
Xue, N., Xia, F., Chiou, F.D., et al.: the Penn Chinese TREEBANK: phrase structure annotation of a large corpus. Nat. Lang. Eng. 11(2), 207–238 (2005)
Acknowledgement
This work is supported by Basic and Applied Basic Research Foundation Project of Guangdong Province (2020A1515011056), Foundation of the Guangdong 13th Five-year Plan of Philosophy and Social Sciences (GD19CYY05), General Project of National Scientific and Technical Terms Review Committee (YB2019013), Special innovation project of Guangdong Education Department (2017KTSCX064), Bidding Project of GDUFS Laboratory of Language Engineering and Computing (LEC2019ZBKT002).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Feng, W., Huang, X., Ren, H. (2020). Analyzing Relational Semantics of Clauses in Chinese Discourse Based on Feature Structure. In: Zhu, X., Zhang, M., Hong, Y., He, R. (eds) Natural Language Processing and Chinese Computing. NLPCC 2020. Lecture Notes in Computer Science(), vol 12431. Springer, Cham. https://doi.org/10.1007/978-3-030-60457-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-60457-8_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60456-1
Online ISBN: 978-3-030-60457-8
eBook Packages: Computer ScienceComputer Science (R0)
