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Focus-sensitive relation disambiguation for implicit discourse relation detection |
Yu HONG1( ), Siyuan DING1, Yang XU1, Xiaoxia JIANG2, Yu WANG2, Jianmin YAO1, Qiaoming ZHU1, Guodong ZHOU1 |
1. Natural Language Processing Lab, School of Computer Science & Technology, Soochow University, Suzhou 215006, China
2. Science and Technology on Information Systems Engineering Laboratory, Nanjing 210007, China |
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Abstract We study implicit discourse relation detection, which is one of the most challenging tasks in the field of discourse analysis. We specialize in ambiguous implicit discourse relation, which is an imperceptible linguistic phenomenon and therefore difficult to identify and eliminate. In this paper, we first create a novel task named implicit discourse relation disambiguation (IDRD). Second, we propose a focus-sensitive relation disambiguation model that affirms a truly-correct relation when it is triggered by focal sentence constituents. In addition, we specifically develop a topicdriven focus identification method and a relation search system (RSS) to support the relation disambiguation. Finally, we improve current relation detection systems by using the disambiguation model. Experiments on the penn discourse treebank (PDTB) show promising improvements.
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| Keywords
Implicit discourse relation
focus-sensitive implicit relation disambiguation
topic-driven focus identification
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Corresponding Author(s):
Yu HONG
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Just Accepted Date: 19 June 2017
Online First Date: 03 December 2018
Issue Date: 19 July 2019
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