Semantic parsing for high-precision semantic role labelling

  • Authors:
  • Paola Merlo;Gabriele Musillo

  • Affiliations:
  • University of Geneva, Genève, Switzerland;University of Geneva, Genève, Switzerland

  • Venue:
  • CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper, we report experiments that explore learning of syntactic and semantic representations. First, we extend a state-of-the-art statistical parser to produce a richly annotated tree that identifies and labels nodes with semantic role labels as well as syntactic labels. Secondly, we explore rule-based and learning techniques to extract predicate-argument structures from this enriched output. The learning method is competitive with previous single-system proposals for semantic role labelling, yields the best reported precision, and produces a rich output. In combination with other high recall systems it yields an F-measure of 81%.