Composition of Word Representations Improves Semantic Role Labelling

State-of-the-art semantic role labelling systems require large annotated corpora to achieve full performance. Unfortunately, such corpora are expensive to produce and often do not generalize well across do-mains. Even in domain, errors are often made where syntactic information does not provide sufficient cues. In this pa-per, we mitigate both of these problems by employing distributional word repre-sentations gathered from unlabelled data. While straight-forward word representa-tions of predicates and arguments improve performance, we show that further gains are achieved by composing representa-tions that model the interaction between predicate and argument, and capture full argument spans. 1