In this paper we present a novel, customizable IE paradigm that takes advantage of predicate-argument structures. We also introduce a new way of automatically identifying predicate argument structures, which is central to our IE paradigm. It is based on: (1) an extended set of features; and (2) inductive decision tree learning. The experimental results prove our claim that accurate predicate-argument structures enable high quality IE results.
CITATION STYLE
Surdeanu, M., Harabagiu, S., Williams, J., & Aarseth, P. (2003). Using predicate-argument structures for information extraction. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (Vol. 2003-July). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1075096.1075098
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