Joint Parsing and Semantic Role Labeling

Abstract

A striking feature of human syntactic processing is that it is context-dependent, that is, it seems to take into account semantic information from the discourse context and world knowledge. In this paper, we attempt to use this insight to bridge the gap between SRL results from gold parses and from automatically-generated parses. To do this, we jointly perform parsing and semantic role labeling, using a probabilistic SRL system to rerank the results of a probabilistic parser. Our current results are negative, because a locally trained SRL model can return inaccurate probability estimates.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA439390

Entities

People

  • Andrew McCallum
  • Charles Sutton

Organizations

  • University of Massachusetts Amherst

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Base Lines
  • Computational Linguistics
  • Computer Science
  • Dynamic Programming
  • Information Operations
  • Information Retrieval
  • Joints
  • Language
  • Linguistics
  • Machine Learning
  • Models
  • Natural Languages
  • Probabilistic Models
  • Probability
  • Test Sets
  • Training

Fields of Study

  • Computer science
  • Engineering

Readers

  • Computational Linguistics
  • Structural Health Monitoring of Composite Structures.
  • Theoretical Analysis.