JAIST: A two-phase machine learning approach for identifying discourse relations in newswire texts
Son Nguyen, Quoc Ho, Le-Minh Nguyen · 2015
In this paper, we present a machine learning approach for identifying shallow discourse relations in news wire text.Our approach has 2 phases.The arguments detection phase will identify arguments and explicit connectives by using the Conditional Random Fields (CRFs) learning algorithm with a set of features such as words, parts of speech (POS) and features extracted from the parsing tree of sentences.The second phase, the sense classification phase, will classify arguments and explicit connectives into one of fifteen types of senses by using the SMO classifier with a simple feature set.The performance of system was evaluated three different data sets given by the CoNLL 2015 Shared Task.The parser of our system was ranked 4 of 16 participating systems on F-measure when evaluating on the blind data set (strict matching).