Combining Seemingly Incompatible Corpora for Implicit Semantic Role Labeling

Implicit semantic role labeling, the task of retrieving locally unrealized arguments from wider discourse context, is a knowledgeintensive task. At the same time, the annotated corpora that exist are all small and scattered across different annotation frameworks, genres, and classes of predicates. Previous work has treated these corpora as incompatible with one another, and has concentrated on optimizing the exploitation of single corpora. In this paper, we show that corpus combination is effective after all when the differences between corpora are bridged with domain adaptation methods. When we combine the SemEval-2010 Task 10 and Gerber and Chai noun corpora, we obtain substantially improved performance on both corpora, for all roles and parts of speech. We also present new insights into the properties of the implicit semantic role labeling task.