Abstract
In this paper two systems participating to the Evalita Frame Labeling over Italian Texts challenge are presented. The first one, i.e. the SVM-SPTK system, implements the Smoothed Partial Tree Kernel that models semantic roles by implicitly combining syntactic and lexical information of annotated examples. The second one, i.e. the SVM-HMM system, realizes a flexible approach based on the Markovian formulation of the SVM learning algorithm. In the challenge, the SVM-SPTK system obtains state-of-the-art results in almost all tasks. Performances of the SVM-HMM system are interesting too, i.e. the second best scores in the Frame Prediction and Argument Classification tasks, especially considering it does not rely on a full syntactic parsing.
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Croce, D., Bastianelli, E., Castellucci, G. (2013). Structured Kernel-Based Learning for the Frame Labeling over Italian Texts. In: Magnini, B., Cutugno, F., Falcone, M., Pianta, E. (eds) Evaluation of Natural Language and Speech Tools for Italian. EVALITA 2012. Lecture Notes in Computer Science(), vol 7689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35828-9_24
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DOI: https://doi.org/10.1007/978-3-642-35828-9_24
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