Abstract：Automatic discourse processing is considered as one of the most challenging NLP tasks which is helpful to many downstream NLP tasks, such as question answering, automatic summary and natural language generation. Recently, the large scale discourse corpus PDTB is made available, which provides a common platform for discourse researchers. On the basis of PDTB corpus, the paper proposes an end-to-end explicit discourse parser with conditional random fields. The parser consists of three components joined in a sequential pipeline architecture, which includes connective classifier, explicit relation classifier and relation argument extractor. We report the performance on each component, and, from error-cascading perspectives, we analyses the parsers overall performance in detail.
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2016, Vol. 30 
