Data-Driven Approach Using Semantics for Recognizing and Classifying TimeML Events in Italian

摘要

We present a data-driven approach for recognizing and classifying TimeML events in Italian. A high-performance state-of-the-art approach, TIPSem, is adopted and extended with Italian-specific semantic features from a lexical resource. The resulting approach has been evaluated over the official TempEval2 Italian test data. The analysis of the results shows a positive impact of the semantic features both for event recognition and classification. Moreover, the presented data-driven approach has been compared with an existing rule-b?sed prototype over the same data set. The results are directly comparable and show that the machine learning strategy better deals with the complexity of the tasks.