Semantic role labeling using lexical statistical information
Simone Paolo Ponzetto, Michael Strube · 2005
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 shallow 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 evaluate the performance on a set of relatively 'cheap' features and report an F 1 score of 68.13% on the overall test set.