Abstract
Event detection is a hot and difficult problem in information extraction. It is widely used in automatic news extraction, financial event analysis and other fields. However, most of the existing event detection methods only focus on a single language, ignoring the event information provided by other languages, and can not solve the problem of polysemy in a single language, which makes it difficult to improve the performance of event detection methods. To solve these problems, this paper proposes a new Event Detection based on Multilingual Information Enhanced Syntactic Dependency GCN. Specifically, the model translates the original language and aligns words, takes multilingual data as input, and constructs syntactic dependency diagrams for initial language sentences. Then, a graph neural network is constructed based on the syntactic dependency graph, and combined with the attention mechanism, the nodes of the syntactic dependency graph are enhanced by the translated language. Finally, the classifier finds the trigger and judges the event type. The model effectively improves the recognition efficiency of polysemous words by using multilingual information, and makes full use of sentence structure information by using syntactic dependency graph. Experiments on ace2005 benchmark data set show that the model can detect events effectively and is obviously superior to the existing event detection methods.
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This work was supported by National Natural Science Foundation of China (No. 61931019).
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Wang, Z., Li, B., Wang, Y. (2022). Event Detection Based on Multilingual Information Enhanced Syntactic Dependency GCN. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13370. Springer, Cham. https://doi.org/10.1007/978-3-031-10989-8_30
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DOI: https://doi.org/10.1007/978-3-031-10989-8_30
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