What a Parser Can Learn from a Semantic Role Labeler and Vice Versa

In many NLP systems, there is a unidirectional flow of information in which a parser supplies input to a semantic role labeler. In this paper, we build a system that allows information to flow in both directions. We make use of semantic role predictions in choosing a single-best parse. This process relies on an averaged perceptron model to distinguish likely semantic roles from erroneous ones. Our system penalizes parses that give rise to low-scoring semantic roles. To explore the consequences of this we perform two experiments. First, we use a baseline generative model to produce n-best parses, which are then re-ordered by our semantic model. Second, we use a modified version of our semantic role labeler to predict semantic roles at parse time. The performance of this modified labeler is weaker than that of our best full SRL, because it is restricted to features that can be computed directly from the parser's packed chart. For both experiments, the resulting semantic predictions...

• Type: Journal
• Year: 2010
• Where: EMNLP
• Authors: Stephen A. Boxwell, Dennis Mehay, Chris Brew
• Keywords: EMNLP 2010, Low-scoring Semantic Roles, Natural Language Processing, Semantic Role Labeler, Semantic Roles