Affiliations: Department of Computer Science, Katholieke Universiteit
Leuven, Celestijnenlaan 200 A, B-3001 Heverlee, Belgium.
{niels.landwehr,bernd.gutmann,ingo.thon,luc.deraedt}@cs.kuleuven.be | Intel Research Seattle, 1100 NE 45th Street, Seattle,
WA 98105, USA. [email protected]
Note: [] Address for correspondence: Department of Computer Science,
Katholieke Universiteit Leuven, Celestijnenlaan 200 A, B- 3001 Heverlee,
Belgium
Abstract: The ability to recognize human activities from sensory information
is essential for developing the next generation of smart devices. Many human
activity recognition tasks are – from a machine learning
perspective – quite similar to tagging tasks in natural language
processing. Motivated by this similarity, we develop a relational
transformation-based tagging system based on inductive logic programming
principles, which is able to cope with expressive relational representations as
well as a background theory. The approach is experimentally evaluated on two
activity recognition tasks and an information extraction task, and compared to
Hidden Markov Models, one of the most popular and successful approaches for
tagging.
Keywords: relational learning, sequence tagging, activity recognition, information extraction
