A Constituent-Based Approach to Argument Labeling with Joint Inference in Discourse Parsing
Fang Kong, Hwee Tou Ng, Guodong Zhou · 2014
Discourse parsing is a challenging task and plays a critical role in discourse analysis.In this paper, we focus on labeling full argument spans of discourse connectives in the Penn Discourse Treebank (PDTB).Previous studies cast this task as a linear tagging or subtree extraction problem.In this paper, we propose a novel constituent-based approach to argument labeling, which integrates the advantages of both linear tagging and subtree extraction.In particular, the proposed approach unifies intra-and intersentence cases by treating the immediately preceding sentence as a special constituent.Besides, a joint inference mechanism is introduced to incorporate global information across arguments into our constituent-based approach via integer linear programming.Evaluation on PDT-B shows significant performance improvements of our constituent-based approach over the best state-of-the-art system.It also shows the effectiveness of our joint inference mechanism in modeling global information across arguments.