Abstract
Event Detection (ED) is a task that aims to recognize triggers and identify the event type in sentences. Syntactic information plays a crucial role in event detection model accurately to recognize the triggers and event type. The previous works commonly use word embedding to obtain context representation that cannot fully exploit syntactic information. In this paper, we propose a novel model HSGCN (Hybrid Syntactic Graph Convolutional Networks) for Chinese event detection, which utilizes graph convolutional networks to generate sentence-level feature and exploit a task-specific hybrid representation considering both character-level feature and word-level feature. Our experiments demonstrate that HSGCN model can capture rich syntactic to improve identifying the triggers and event type. Compared with existing models, HSGCN can achieve efficient and accurate results on ACE 2005 and KBPEval2017 datasets. In trigger identification and type classification tasks, HSGCN respectively achieved 70.2\(\%\) and 65.7\(\%\) F-score with average 1.2\(\%\) and 0.9\(\%\) absolute improvement compare to state-of-the-art method.
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Acknowledgements
This research was funded by the National Natural Science Foundation of China, grant number 61402220, the Philosophy and Social Science Foundation of Hunan Province, grant number 16YBA323, the Scientic Research Fund of Hunan Provincial Education Department for excellent talents, grant number 18B279, the key program of Scientic Research Fund of Hunan Provincial Education Department, grant number 19A439, the Project supported by the Natural Science Foundation of Hunan Province, China, grant number 2020JJ4525.
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Ma, X., Liu, Y., Ouyang, C. (2021). Hybrid Syntactic Graph Convolutional Networks for Chinese Event Detection. In: Chen, H., Liu, K., Sun, Y., Wang, S., Hou, L. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence. CCKS 2020. Communications in Computer and Information Science, vol 1356. Springer, Singapore. https://doi.org/10.1007/978-981-16-1964-9_12
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