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
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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