Relational Learning for Spatial Relation Extraction from Natural Language

  • Parisa Kordjamshidi
  • Paolo Frasconi
  • Martijn Van Otterlo
  • Marie-Francine Moens
  • Luc De Raedt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7207)

Abstract

Automatically extracting spatial information is a challenging novel task with many applications. We formalize it as an information extraction step required for a mapping from natural language to a formal spatial representation. Sentences may give rise to multiple spatial relations between words representing landmarks, trajectors and spatial indicators. Our contribution is to formulate the extraction task as a relational learning problem, for which we employ the recently introduced kLog framework. We discuss representational and modeling aspects, kLog’s flexibility in our task and we present current experimental results.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Parisa Kordjamshidi
    • 1
  • Paolo Frasconi
    • 2
  • Martijn Van Otterlo
    • 3
  • Marie-Francine Moens
    • 1
  • Luc De Raedt
    • 1
  1. 1.Department of Computer ScienceKatholieke Universiteit LeuvenBelgium
  2. 2.Università degli Studi di FirenzeItaly
  3. 3.Radboud University NijmegenThe Netherlands

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