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| Recognizing PDTB Style Implicit Discourse Relations |
| LI Sheng, KONG Fang, ZHOU Guodong |
| School of Computer Sciences and Technology, Soochow University, Suzhou, Jiangsu 215006, China |
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Abstract Recognizing implicit discourse relation is a challenging task in discourse parsing. In this paper, we propose an implicit discourse relation recognizing method in the Penn Discourse Treebank (PDTB) considering some traditional features (e.g., verbs, polarity, production rules, and so on), and provide a systematic analysis for our implicit discourse relation method. We apply all labeled data to build multiple classifiers, and use the adding rule to identify final classification result for each instance. We also use forward feature selection method to select an optimal feature subset for each classification task. Experimental results in the PDTB corpus show that our proposed method can significantly improve the state-of-the-art performance of recognizing implicit discourse relation.
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Received: 05 May 2014
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2016, Vol. 30 
