Paper: Semantic Role Labelling With Chunk Sequences

ACL ID W04-2413
Title Semantic Role Labelling With Chunk Sequences
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2004
Authors

We describe a statistical approach to semantic role labelling that employs only shallow infor- mation. We use a Maximum Entropy learner, augmented by EM-based clustering to model the fit between a verb and its argument can- didate. The instances to be classified are se- quences of chunks that occur frequently as ar- guments in the training corpus. Our best model obtains an F score of 51.70 on the test set.