Abstract
Event co-reference and event temporal relations are two important types of event relations, which are widely used in many NLP applications, such as information extraction, text summarization, question answering system, etc. Event temporal relations provide much useful semantic and discourse information for more accurate co-reference resolution. However, traditional event co-reference resolution neglects those event temporal relations, leading to inconsistent resolutions in temporal logic. This paper proposes a joint model for event co-reference resolution and event temporal relation identification in Chinese event corpus. The experimental results on the ACE corpus show that our model can improve the performance of the above two tasks.
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Teng, J., Li, P., Zhu, Q., Ge, W. (2016). Joint Event Co-reference Resolution and Temporal Relation Identification. In: Dong, M., Lin, J., Tang, X. (eds) Chinese Lexical Semantics. CLSW 2016. Lecture Notes in Computer Science(), vol 10085. Springer, Cham. https://doi.org/10.1007/978-3-319-49508-8_40
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DOI: https://doi.org/10.1007/978-3-319-49508-8_40
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