Data-Driven Approach Based on Semantic Roles for Recognizing Temporal Expressions and Events in Chinese

This paper addresses the automatic recognition of temporal expressions and events in Chinese. For this language, these tasks are still in an exploratory stage and high-performance approaches are needed. Recently, in TempEval-2 evaluation exercise, corpora annotated in TimeML were released for different languages including Chinese. However, no systems were evaluated in this language. We present a data-driven approach for addressing these tasks in Chinese, TIRSemZH. This uses semantic roles, in addition to morphosyntactic information, as feature. The performance achieved by TIRSemZH over the TempEval-2 Chinese data (85% F1) is comparable to the state of the art for other languages. Therefore, the method can be used to develop high-performance temporal processing systems, which are currently not available for Chinese. Furthermore, the results obtained verify that when semantic roles are applied, the performance of a baseline based only on morphosyntax is improved. This supports and extends the conclusions reached by related works for other languages.