Abstract：Discourse relation analysis is a task of natural language understanding which aimed at analyzing and disposing the semantic relation and rhetorical structure of discourse. Implicit discourse relation analysis is an important subtask of automatically detectind senses of semantic relation between arguments in the absence of direct cues. Currently, the performance of implicit discourse relation analysis is low and state-of-art accuracy can only reach 40%. The major cause of this situation is that the existing methods did not analyze arguments in the semantic frame, limited only to the local features and correlation analysis of arguments. This paper proposes a method of implicit discourse relation inference based on frame semantic. This method automatic recognised semantic frame of arguments through FrameNet and related identification technology. On this basis, we indentify the semantic relation of arguments by the distribution probability of frame semantic relation in large-scale text data. The experimental results show that, only using the first level of frame semantic can improve the detection performance of implicit discourse relation up to 5.14%; meanwhile, this method can make the accuracy rate increased by 10.68% in the case of considering the balance of relation categories.
[1] Prasad R, Dinesh N, Lee A, et al. The Penn Discourse Treebank 2.0[C]//Proceedings of the 6th International Conference on Language Resources and Evaluation (LREC), Marrakech, Morocco, 2008: 2961-2968. [2] Miltsakaki E, Robaldo L, Lee A, et al. Sense annotation in the Penn Discourse Treebank. Journal of Computational Linguistics and Intelligent Text Processing, Lecture Notes in Computer Science, 2008, 4919: 275-286. [3] Riaz M, Girju R. Another look at causality: Discovering Scenario-specific Contingency Relationships with no Supervision[C]//Proceedings of the 4th IEEE International Conference on Semantic Computing (ICSC), Pittsburgh, USA, 2010: 361-368. [4] Do QX, Chan YS, Roth D. Minimally supervised event causality identification[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Edinburgh,UK,2011: 294-303. [5] Zhou L, Li B, Gao W, et al. Unsupervised discovery of discourse relations for eliminating intra-sentence polarity ambiguities[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Edinburgh, Scotland, UK 2011: 162-171. [6] 王继成,武港山,周源远等. 一种篇章结构指导的中文Web文档自动摘要方法[J]. 计算机研究与发展, 2003, 40(3): 398-405. [7] Pitler E, Raghupathy M, Mehta H, et al. Easily identifiable discourse relations[C]//Proceedings of the 22nd International Conference on Computational Linguistics (COLING), Manchester, UK, 2008: 87-90. [8] Wang Wen-Ting, Su Jian, Tan CL. Kernel based discourse relation recognition with temporal ordering information[C]//Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL), Uppsala, Sweden, 2010: 710-719. [9] Zhou Zhi-Min, Xu Yu, Niu Zheng-Yu, et al. Predicting discourse connectives for implicit discourse relation recognition[C]//Proceedings of the 23th International Conference on Computational Linguistics (COLING), Poster, Beijing, China, 2010: 1507-1514. [10] Napoles C, Gormley M, Durme BV. Annotated Gigaword[C]//Proceedings of the Joint Workshop on Automatic Knowledge Base Construction & Web-scale Knowledge Extraction (AKBC-WEKEX) of NAACL-HLT, Montreal, Canada, 2012: 95-100. [11] Lin ZH, Kan MY, Ng HT. Recognizing implicit discourse relations in the Penn Discourse Treebank[C]//Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (EMNLP), Singapore, 2009: 343-351. [12] Soricut R, Marcu D. Sentence level discourse parsing using syntactic and lexical information[C]//Proceedings of the 2003 Conference of the North America Chapter of the Association for Computational Linguistics on Human Language Technology (NAACL), Edmonton, Canada, 2003: 149-156. [13] Marcu D, Echihabi A. An unsupervised approach to recognizing discourse relations[C]//Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Morristown, NJ, USA, 2002: 368-375. [14] Saito M, Yamamoto K, Sekine S. Using phrasal patterns to identify discourse relations[C]//Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT-NAACL), New York, USA, 2006: 133-136. [15] Fillmore CJ. Frame semantics and the nature of language[J]. Annals of the New York Academy of Sciences, 1976: 20-32.
2015, Vol. 29 
