Open AccessProceedings Article
Kernel Based Discourse Relation Recognition with Temporal Ordering Information
Wenting Wang,Jian Su,Chew Lim Tan +2 more
- 11 Jul 2010
- pp 710-719
TL;DR: This paper proposes 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, and shows tree kernel approach is able to give statistical significant improvements over flat syntactic path feature.
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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 2.0 for both explicit and implicit as well.
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Citations
•Proceedings Article
Leveraging Synthetic Discourse Data via Multi-task Learning for Implicit Discourse Relation Recognition
Man Lan,Yu Xu,Zheng-Yu Niu +2 more
- 01 Aug 2013
TL;DR: Results on PDTB data show that under the multi-task learning framework the authors' models with the use of the prediction of explicit discourse connectives as auxiliary learning tasks, can achieve an averaged F1 improvement of 5.86% over baseline models.
56
A PDTB-Styled End-to-End Discourse Parser
TL;DR: This article proposed an end-to-end discourse parser to parse free texts in the PDTB style in a fully data-driven approach, which consists of multiple components joined in a sequential pipeline architecture, which includes a connective classifier, argument labeler, explicit classifier and nonexplicit classifier.
•Proceedings Article
Implicit Discourse Relation Recognition by Selecting Typical Training Examples
Xun Wang,Sujian Li,Jiwei Li,Wenjie Li +3 more
- 01 Dec 2012
TL;DR: This paper is the first time to apply a d ifferent typical/atypical perspective to select the most suitable discourse relation examples as training data and proves that the proposed new method outperforms the state -of-the-art methods.
39
•Proceedings Article
Modelling Discourse Relations for Arabic
Amal Alsaif,Katja Markert +1 more
- 27 Jul 2011
TL;DR: The first algorithms to automatically identify explicit discourse connectives and the relations they signal for Arabic text are presented, and the algorithm for recognizing discourse relations performs significantly better than a baseline based on the connective surface string alone and therefore reduces the ambiguity in explicit connective interpretation.
33
•Proceedings Article
Exploiting Discourse Relations for Sentiment Analysis
Fei Wang,Yunfang Wu,Likun Qiu +2 more
- 01 Dec 2012
TL;DR: This paper utilizes explicit connectives to predict discourse relations, and proposes several methods to incorporate discourse relation knowledge to the task of sentiment analysis, validating the effectiveness of discourse relations in Chinese sentiment analysis.
24
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TL;DR: The second version of the Penn Discourse Treebank, PDTB-2.0, is presented, describing its lexically-grounded annotations of discourse relations and their two abstract object arguments over the 1 million word Wall Street Journal corpus.