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Latent Relational Model for Relation Extraction

  • Gaetano RossielloEmail author
  • Alfio Gliozzo
  • Nicolas Fauceglia
  • Giovanni Semeraro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11503)

Abstract

Analogy is a fundamental component of the way we think and process thought. Solving a word analogy problem, such as mason is to stone as carpenter is to wood, requires capabilities in recognizing the implicit relations between the two word pairs. In this paper, we describe the analogy problem from a computational linguistics point of view and explore its use to address relation extraction tasks. We extend a relational model that has been shown to be effective in solving word analogies and adapt it to the relation extraction problem. Our experiments show that this approach outperforms the state-of-the-art methods on a relation extraction dataset, opening up a new research direction in discovering implicit relations in text through analogical reasoning.

Keywords

Information extraction Distributional semantics 

Notes

Acknowledgement

This work was conducted during an internship at the IBM Thomas J. Watson Research Center in Yorktown Heights, NY, USA. We thank Anastas Stoyanovsky, Steven Pritko and Gabe Hart, software engineers at the IBM Watson Groups in Pittsburgh and Denver, USA, for helping and inspiring us during the “Fast Domain Adaptation in IBM Watson Discovery” project.

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Authors and Affiliations

  • Gaetano Rossiello
    • 1
    Email author
  • Alfio Gliozzo
    • 2
  • Nicolas Fauceglia
    • 2
  • Giovanni Semeraro
    • 1
  1. 1.Department of Computer ScienceUniversity of BariBariItaly
  2. 2.IBM Research AIYorktown HeightsUSA

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