Structured Kernel-based Learning for Spatial Role Labeling
Emanuele Bastianelli PhD Candidate

This event took place on 3rd July 2013 at 11:30am (10:30 GMT)
Knowledge Media Institute, Berrill Building, The Open University, Milton Keynes, United Kingdom, MK7 6AA

Referring to objects or entities in the space, as well as to relations holding among them, is one of the most important functionality in natural language understanding. As a result, the detection of spatial utterances finds many applications, such as in Spatal Relation Extraction, GPS navigation systems, or Human-Robot Interaction (HRI). In this presentation a system that participated to the Spatial Role Labeling SemEval task will be presented. The spatial roles classification is addressed as a sequence-based word classification problem: the SVM-hmm learning algorithm is applied, based on a simple feature modeling and a robust lexical generalization achieved through a Distributional Models of Lexical Semantics. In the identification of spatial relations, all roles found in a sentence are combined to generate candidate relations, later verified by a SVM classifier. The Smoothed Partial Tree Kernel is here applied, i.e. a convolution kernel that enhances both syntactic and lexical properties of the examples.


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