Kernel Based Discourse Relation Recognition with Temporal Ordering Information

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 710–719, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Kernel Based Discourse Relation Recognition with Temporal Ordering Information WenTing Wang1 Jian Su1 Chew Lim Tan2 1Institute for Infocomm Research 1 Fusionopolis Way, #21-01 Connexis Singapore 138632 {wwang,sujian}@i2r.a-star.edu.sg 2Department of Computer Science University of Singapore Singapore 117417 tacl@comp.nus.edu.sg Abstract Syntactic knowledge is important for dis- course relation recognition. Yet only heu- ristically selected flat paths and 2-level production rules have been used to incor- porate such information so far. In this paper we propose using tree kernel based approach to automatically mine the syn- tactic information from the parse trees for discourse analysis, applying kernel func- tion to the tree structures directly. These structural syntactic features, together with other normal flat features are incor- porated into our composite kernel to cap- ture diverse knowledge for simultaneous discourse identification and classification for both explicit and implicit relations. The experiment shows tree kernel ap- proach is able to give statistical signifi- cant improvements over flat syntactic path feature. We also illustrate that tree kernel approach covers more structure in- formation than the production rules, which allows tree kernel to further incor- porate information from a higher dimen- sion space for possible better discrimina- tion. Besides, we further propose to leve- rage on temporal ordering information to constrain the interpretation of discourse relation, which also demonstrate statistic- al significant improvements for discourse relation recognition on PDTB 2.0 for both explicit and implicit as well. 1 Introduction Discourse relations capture the internal structure and logical relationship of coherent text, includ- ing Temporal, Causal and Contrastive relations etc. The ability of recognizing such relations be- tween text units including identifying and classi- fying provides important information to other natural language processing systems, such as language generation, document summarization, and question answering. For example, Causal relation can be used to answer more sophisti- cated, non-factoid ‘Why’ questions. Lee et al. (2006) demonstrates that modeling discourse structure requires prior linguistic anal- ysis on syntax. This shows the importance of syntactic knowledge to discourse analysis. How- ever, most of previous work only deploys lexical and semantic features (Marcu and Echihabi, 2002; Pettibone and PonBarry, 2003; Saito et al., 2006; Ben and James, 2007; Lin et al., 2009; Pit- ler et al., 2009) with only two exceptions (Ben and James, 2007; Lin et al., 2009). Nevertheless, Ben and James (2007) only uses flat syntactic path connecting connective and arguments in the parse tree. The hierarchical structured informa- tion in the trees is not well preserved in their flat syntactic path features. Besides, such a syntactic feature selected and defined according to linguis- tic intuition has its limitation, as it remains un- clear what kinds of syntactic heuristics are effec- tive for discourse analysis. The more recent work from Lin et al. (2009) uses 2-level production rules to represent parse tree information. Yet it doesn’t cover all the oth- er sub-trees structural information which can be …

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