A generative model for semantic role labeling

Abstract

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 null- instantiated roles, which commonly occur in our corpus and whose identification is crucial to natural language interpretation.

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 74,649

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

  • Only published works are available at libraries.

Analytics

Added to PP
2009-01-28

Downloads
31 (#374,239)

6 months
1 (#419,510)

Historical graph of downloads
How can I increase my downloads?