A Semantic Feature for Verbal Predicate and Semantic Role Labeling using SVMs

Hansen A. Schwartz, Fernando Gomez, Christopher Millward

This paper shows that semantic role labeling is a consequence of accurate verbal predicate labeling. In doing so, the paper presents a novel type of semantic feature for verbal predicate labeling using a new corpus. The corpus contains verbal predicates, serving as verb senses, that have semantic roles associated with each argument. Although much work has been done using feature vectors with machine learning algorithms for various types of semantic classification tasks, past work has primarily shown effective use of syntactic or lexical information. Our new type of semantic feature, ontological regions, proves highly effective when used in addition to or in place of syntactic and lexical features for support vector classification, increasing accuracy of verbal predicate labeling from 65.4% to 78.8%.

Subjects: 13. Natural Language Processing; 12.1 Reinforcement Learning

Submitted: Feb 25, 2008

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