Semantic role labeling using lexical statistical information

Authors:
Simone Paolo Ponzetto;Michael Strube
Affiliations:
EML Research gGmbH, Heidelberg, Germany;EML Research gGmbH, Heidelberg, Germany
Venue:
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Year:
2005

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Abstract

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 F1 score of 68.13% on the overall test set.