Proceedings of the 21st International Conference on Inductive Logic Programming vol:7207 pages:347-357
Lecture Notes in Artificial Intelligence
Inductive Logic Programming edition:21 location:Windsor Great Park, UK date:31 July 2011 - 3 August 2011
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 classication 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.