Event co-reference resolution is an important task in natural language processing, and nearly all the existing approaches for this task rely on event argument information. However, these methods tend to suffer from error propagation from event argument extraction. Additionally, not every event mention contains all arguments of an event, and the argument information may confuse the model where events contain arguments to detect an event co-reference in real text. Furthermore, the context information of an event is useful to infer the co-reference between events. Thus, to reduce the errors propagated from event argument extraction and use context information effectively, we propose a multi-loss neural network model that does not require any argument information relating to the within-document event co-reference resolution task; furthermore, it achieves a significantly better performance than the state-of-the-art methods.
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This work was supported by National Natural Science Foundation of China (Grant Nos. 61533018, 61806201, 61702512), Independent Research Project of National Laboratory of Pattern Recognition. This work was also supported by CCF-Tencent Open Fund.
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Zuo, X., Chen, Y., Liu, K. et al. Event co-reference resolution via a multi-loss neural network without using argument information. Sci. China Inf. Sci. 62, 212101 (2019). https://doi.org/10.1007/s11432-018-9833-1
- event co-reference resolution
- neural network
- information extraction
- multi-loss function
- event extraction