A Generative Model for Semantic Role Labeling

Determining the semantic role of sentence constituents is a key task in determining sentence meanings lying behind a veneer of variant syntactic expression. We present a model of natural language generation from semantics using the FrameNet semantic role and frame ontology. We train the model using the FrameNet corpus and apply it to the task of automatic semantic role and frame identification, producing results competitive with previous work (about 70% role labeling accuracy). Unlike previous models used for this task, our model does not assume that the frame of a sentence is known, and is able to identify nullinstantiated roles, which commonly occur in our corpus and whose identification is crucial to natural language interpretation.

• Type: Conference
• Year: 2003
• Where: ECML
• Authors: Cynthia A. Thompson, Roger Levy, Christopher D. Manning
• Keywords: Automatic Semantic Role, ECML 2003, FrameNet Semantic Role, Semantic Role