Abstract
We develop an unsupervised semantic role labelling system that relies on the direct application of information in a predicate lexicon combined with a simple probability model. We demonstrate the usefulness of predicate lexicons for role labelling, as well as the feasibility of modifying an existing role-labelled corpus for evaluating a different set of semantic roles. We achieve a substantial improvement over an informed baseline. © 2005 Association for Computational Linguistics.
Cite
CITATION STYLE
Swier, R. S., & Stevenson, S. (2005). Exploiting a verb lexicon in automatic semantic role labelling. In HLT/EMNLP 2005 - Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 883–890). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1220575.1220686
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