OPT: Oslo-Potsdam-Teesside. Pipelining Rules, Rankers, and Classifier Ensembles for Shallow Discourse Parsing.
CoNLL Shared Task(2016)
摘要
The OPT submission to the Shared Task of the 2016 Conference on Natural Language Learning (CoNLL) implements a ‘classic’ pipeline architecture, combining binary classification of (candidate) explicit connectives, heuristic rules for non-explicit discourse relations, ranking and ‘editing’ of syntactic constituents for argument identification, and an ensemble of classifiers to assign discourse senses. With an end-toend performance of 27.77 F1 on the English ‘blind’ test data, our system advances the previous state of the art (Wang & Lan, 2015) by close to four F1 points, with particularly good results for the argument identification sub-tasks. OPT system results appear more competitive on the new, ‘blind’ test data than on the ‘test’ and ‘development’ sections of the Penn Discourse Treebank (PDTB; Prasad et al., 2008), which may indicate reduced over-fitting to specific properties of the venerable Wall Street Journal (WSJ) text underlying the PDTB.
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