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

ACL ID S13-2096
Title UNITOR-HMM-TK: Structured Kernel-based learning for Spatial Role Labeling
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

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 SVMhmm learning algorithm is applied, based on a simple feature modeling and a ro- bust 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 con- volution kernel that enhances both syntactic and lexical properties of the examples, avoid- ing the need of a manual feature engineering phase. Finally, results on three of the five tasks of the challenge are reported.