UNITOR-HMM-TK: Structured Kernel-based learning for Spatial Role Labeling

In this paper the UNITOR-HMM-TK system participating in the Spatial Role Labeling task at SemEval 2013 is presented. The spatial roles classification is addressed as a sequence-based word classification problem: the SVM learning algorithm is applied, based on a simple feature modeling and a robust lexical generalization achieved through a Distributional Model of Lexical Semantics. In the identification of spatial relations, roles are combined to generate candidate relations, later verified by a SVM classifier. The Smoothed Partial Tree Kernel is applied, i.e. a convolution kernel that enhances both syntactic and lexical properties of the examples, avoiding the need of a manual feature engineering phase. Finally, results on three of the five tasks of the challenge are reported. c 2013 Association for Computational Linguistics