What Information is Helpful for Dependency Based Semantic Role Labeling
Yanyan Luo, Kevin Duh, Yūji Matsumoto · International Joint Conference on Natural Language Processing · 2013
Semantic Role Labeling (SRL) is an important task since it benefits a wide range of natural language processing applications. Given a sentence, the task of SRL is to identify arguments for a predicate (target verb or noun) and assign semantically meaningful labels to them. Dependency parsing based methods have achieved much success in SRL. However, due to errors in dependency parsing, there remains a large performance gap between SRL based on oracle parses and SRL based on automatic parses in practice. In light of this, this paper investigates what additional information is necessary to close this gap. Is it worthwhile to introduce additional dependency information in the form of N-best parse features, or is it better to incorporate orthogonal nondependency information (base chunk constituents)? We compare the above features in a SRL system that achieves state-of-theart results on the CoNLL 2009 Chinese task corpus. Our findings suggest that orthogonal information in the form of constituents is much more helpful in improving dependency based SRL in practice.