Kernel-Based Logical and Relational Learning with kLog for Hedge Cue Detection

  • Mathias Verbeke
  • Paolo Frasconi
  • Vincent Van Asch
  • Roser Morante
  • Walter Daelemans
  • Luc De Raedt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7207)

Abstract

Hedge cue detection is a Natural Language Processing (NLP) task that consists of determining whether sentences contain hedges. These linguistic devices indicate that authors do not or cannot back up their opinions or statements with facts. This binary classification problem, i.e. distinguishing factual versus uncertain sentences, only recently received attention in the NLP community. We use kLog, a new logical and relational language for kernel-based learning, to tackle this problem. We present results on the CoNLL 2010 benchmark dataset that consists of a set of paragraphs from Wikipedia, one of the domains in which uncertainty detection has become important. Our approach shows competitive results compared to state-of-the-art systems.

Keywords

statistical relational learning kernel methods natural language learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mathias Verbeke
    • 1
  • Paolo Frasconi
    • 2
  • Vincent Van Asch
    • 3
  • Roser Morante
    • 3
  • Walter Daelemans
    • 3
  • Luc De Raedt
    • 1
  1. 1.Department of Computer ScienceKatholieke Universiteit LeuvenBelgium
  2. 2.Dipartimento di Sistemi e InformaticaUniversità degli Studi di FirenzeItaly
  3. 3.Department of LinguisticsUniversiteit AntwerpenBelgium

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