Abstract
This paper addresses the automatic recognition of temporal expressions and events in Chinese. For this language, these tasks are still in a 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.
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Llorens, H., Saquete, E., Navarro, B., Li, L., He, Z. (2011). Data-Driven Approach Based on Semantic Roles for Recognizing Temporal Expressions and Events in Chinese. In: Muñoz, R., Montoyo, A., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2011. Lecture Notes in Computer Science, vol 6716. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22327-3_9
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DOI: https://doi.org/10.1007/978-3-642-22327-3_9
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