TAILIEUCHUNG - Báo cáo khoa học: "Kernel Based Discourse Relation Recognition with Temporal Ordering Information"

Syntactic knowledge is important for discourse relation recognition. Yet only heuristically selected flat paths and 2-level production rules have been used to incorporate such information so far. In this paper we propose using tree kernel based approach to automatically mine the syntactic information from the parse trees for discourse analysis, applying kernel function to the tree structures directly. | Kernel Based Discourse Relation Recognition with Temporal Ordering Information WenTing Wang1 Jian Su1 institute for Infocomm Research 1 Fusionopolis Way 21-01 Connexis Singapore 138632 wwang sujian @ Chew Lim Tan2 Department of Computer Science University of Singapore Singapore 117417 tacl@ Abstract Syntactic knowledge is important for discourse relation recognition. Yet only heuristically selected flat paths and 2-level production rules have been used to incorporate such information so far. In this paper we propose using tree kernel based approach to automatically mine the syntactic information from the parse trees for discourse analysis applying kernel function to the tree structures directly. These structural syntactic features together with other normal flat features are incorporated into our composite kernel to capture diverse knowledge for simultaneous discourse identification and classification for both explicit and implicit relations. The experiment shows tree kernel approach is able to give statistical significant improvements over flat syntactic path feature. We also illustrate that tree kernel approach covers more structure information than the production rules which allows tree kernel to further incorporate information from a higher dimension space for possible better discrimination. Besides we further propose to leverage on temporal ordering information to constrain the interpretation of discourse relation which also demonstrate statistical significant improvements for discourse relation recognition on PDTB for both explicit and implicit as well. 1 Introduction Discourse relations capture the internal structure and logical relationship of coherent text including Temporal Causal and Contrastive relations etc. The ability of recognizing such relations between text units including identifying and classifying provides important information to other natural language processing systems such as language generation document

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