Hybrid approach to PDTB-styled Discourse Parsing for CoNLL-2015

Abstract

This paper describes our end-to-end PDTB-styled discourse parser for the CoNLL-2015 shared task. We employed a machine learning-based approach to identify discourse relation between text spans for both explicit and implicit relations and employed a rule-based approach to extract arguments of the discourse relations. In particular, we focus on improving the implicit discourse relation identification. First, we extract adjacent pairs of sentences that have some discourse relationships by exploiting a two-class classifier from an entire document. Second, we assign sense labels for them by utilizing a multiple-class classifier. Our system achieved a 0.316 overall F-score for the development set, 0.249 for the testset and 0.157 for the blind testset.