Reducing Sparsity Improves the Recognition of Implicit Discourse Relations
Junyi Jessy Li, Ani Nenkova · 2014
The earliest work on automatic detec-tion of implicit discourse relations relied on lexical features. More recently, re-searchers have demonstrated that syntactic features are superior to lexical features for the task. In this paper we re-examine the two classes of state of the art representa-tions: syntactic production rules and word pair features. In particular, we focus on the need to reduce sparsity in instance repre-sentation, demonstrating that different rep-resentation choices even for the same class of features may exacerbate sparsity issues and reduce performance. We present re-sults that clearly reveal that lexicalization of the syntactic features is necessary for good performance. We introduce a novel, less sparse, syntactic representation which leads to improvement in discourse rela-tion recognition. Finally, we demonstrate that classifiers trained on different repre-sentations, especially lexical ones, behave rather differently and thus could likely be combined in future systems. 1