Semantic Role Labeling Using Complete Syntactic Analysis

In this paper we introduce a semantic role labeling system constructed on top of the full syntactic analysis of text. The labeling problem is modeled using a rich set of lexical, syntactic, and semantic attributes and learned using one-versus-all AdaBoost classifiers. Our results indicate that even a simple approach that assumes that each semantic argument maps into exactly one syntactic phrase obtains encouraging performance, surpassing the best system that uses partial syntax by almost 6%.

• Publication year

  2005

• Venue

  Conference on Computational Natural Language Learning

• Publication date

  2005-06-29

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.3115/1706543.1706586
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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