Manifold learning for the semi-supervised induction of FrameNet predicates: an empirical investigation

  • Authors:
  • Danilo Croce;Daniele Previtali

  • Affiliations:
  • University of Roma, Tor Vergata;University of Roma, Tor Vergata

  • Venue:
  • GEMS '10 Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics
  • Year:
  • 2010

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Abstract

This work focuses on the empirical investigation of distributional models for the automatic acquisition of frame inspired predicate words. While several semantic spaces, both word-based and syntax-based, are employed, the impact of geometric representation based on dimensionality reduction techniques is investigated. Data statistics are accordingly studied along two orthogonal perspectives: Latent Semantic Analysis exploits global properties while Locality Preserving Projection emphasizes the role of local regularities. This latter is employed by embedding prior FrameNet-derived knowledge in the corresponding non-euclidean transformation. The empirical investigation here reported sheds some light on the role played by these spaces as complex kernels for supervised (i.e. Support Vector Machine) algorithms: their use configures, as a novel way to semi-supervised lexical learning, a highly appealing research direction for knowledge rich scenarios like FrameNet-based semantic parsing.