Title: Kernel-based logical and relational learning with kLog for hedge cue detection
Authors: Verbeke, Mathias
Frasconi, Paolo
Van Asch, Vincent
Morante, Roser
Daelemans, Walter
De Raedt, Luc #
Issue Date: Mar-2012
Publisher: Springer
Host Document: Proceedings of the 21st International Conference on Inductive Logic Programming vol:7207 pages:347-357
Series Title: Lecture Notes in Artificial Intelligence
Conference: Inductive Logic Programming edition:21 location:Windsor Great Park, UK date:31 July 2011 - 3 August 2011
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.
ISBN: 978-3-642-31950-1
ISSN: 0302-9743
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
# (joint) last author

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