Paper: Semantic Role Labeling Using Lexical Statistical Information

ACL ID W05-0633
Title Semantic Role Labeling Using Lexical Statistical Information
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2005
Authors

Our system for semantic role labeling is multi-stage in nature, being based on tree pruning techniques, statistical methods for lexicalised feature encoding, and a C4.5 decision tree classifier. We use both shal- low and deep syntactic information from automatically generated chunks and parse trees, and develop a model for learning the semantic arguments of predicates as a multi-class decision problem. We evalu- ate the performance on a set of relatively ‘cheap’ features and report an F1 score of 68.13% on the overall test set.