Structured Kernel-Based Learning for the Frame Labeling over Italian Texts

摘要

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.