Abstract：The functional connective is a word feature that directly expresses interior semantic relations, structure characteristics and the development trend of context of discourse units. Based on the functional connective, this paper puts forward a kind of methods for predicting relations of implicit discourse. First, this method mines functional connectives at the word and phraselevel, and divides the discourse relationcategory of functional connectives. Secondly, it buildsthe concept model for each type of functional connectives to describe argument attributes connected by functional connectives,and establishes the mapping system between argument concepts and discourse relations; Finally, the predictions of the implicit discoursesemantic relationis realized by statistical strategy to recognize conceptual model of argument and with “concept-relations” mapping system. The experimental results show that, the predicting method byconstructing concept model based on functional connectives, got the significant performance improvementscompared to the existing classification method based on supervised learning.
 M Riaz, R Girju. Another look at causality: Discovering scenario-specific contingency relationships with no supervision[C]//Proceedings of the 4th ICSC, 2010: 361-368.  Q X Do, Y S Chan, D Roth. Minimally supervised event causality identification[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2011: 294-303.  王继成,武港山. 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.  L Zhou, B Li, W Gao, 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), 2011: 162-171.  E Pitler, M Raghupathy, H Mehta, et al. Easily identifiable discourse relations[C]//Proceedings of the 22nd International Conference on the COLING, 2008: 87-90.  W T Wang, J Su, C L Tan. Kernel Based Discourse Relation Recognition with Temporal Ordering Information[C]//Proceedings of the 48th Annual Meeting of the ACL, 2010: 710-719.  R Prasad, N Dinesh, A Lee, et al. The Penn Discourse TreeBank 2.0[C]//Proceedings of Proceedings of the 6th International Conference on LREC 2008, Morocco.  L Carlson, D Marcu, M E Okurowski. Building a discourse-tagged corpus in the framework of rhetorical structure theory[C]//Proceedings of the Second SIGDIAL2001, Denmark, 2001: 1-10.  D Marcu, A Echihabi. An Unsupervised Approach to Recognizing Discourse Relations[C]//Proceedings of the 40th Annual Meeting on the ACL, 2002: 368-375.  M Saito, K Yamamoto, S Sekine. Using Phrasal Patterns to Identify Discourse Relations[C]//Proceedings of the Human Language Technology Conference of the NAACL, 2006: 133-136.  F Wolf, E Gibson. Representing discourse coherence: a corpus-based analysis[C]//Proceedings of the 20th International Conference on the COLING, Morristown, NJ, USA, 2005: 134-140.  E Pitler, A Louis, A Nenkova. Automatic Sense Prediction for Implicit Discourse Relations in Text[C]//Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, 2009, (2): 683-691.  R Soricut, D Marcu. Sentence level discourse parsing using syntactic and lexical information[C]//Proceedings of the HLT/NAACL, 2003: 149-156.  Z Lin, H T Ng, M Y Kan. Automatically Evaluating Text Coherence Using Discourse Relations[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011, (2): 997-1006.  Z M Zhou, Y Xu, Z Y Niu, et al. Predicting Discourse Connectives for Implicit Discourse Relation Recognition[C]//Proceedings of the 23rd International Conference on Computational Linguistics: Posters, 2010: 1507-1514.  http://www.bioinf.jku.at/software/apcluster/[DB/OL].  http://nlp.stanford.edu/software/lex-parser.shtml[DB/OL].  E Pitler, A Nenkova. Revisiting readability: A unified framework for predicting text quality[C]//Proceedings of the Conference on the EMNLP, 2008: 186-195.