Paper: Composition of Word Representations Improves Semantic Role Labelling

ACL ID D14-1045
Title Composition of Word Representations Improves Semantic Role Labelling
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

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.