Abstract
Event detection is one of the key tasks to construct knowledge graph and reason graph, also a hot and difficult problem in information extraction. Automatic event detection from unstructured natural language text has far-reaching significance for human cognition and intelligent analysis. However, limited by the source and genre, corpora for event detection can not provide enough information to solve the problems of polysemy, synonym association and lack of information. To solve these problems, this paper proposes a brand new Event Detection model based on Extensive External Information (EDEEI). The model employs external corpus, semantic network, part of speech and attention map to extract complete and accurate triggers. Experiments on ACE 2005 benchmark dataset show that the model effectively uses the external knowledge to detect events, and is significantly superior to the state-of-the-art event detection methods.
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This work was supported by National Natural Science Foundation of China (No. 61931019) and National Key Research and Development Program Project (No. 2019QY2404).
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Wang, Z., Wang, S., Zhang, L., Wang, Y. (2021). Exploiting Extensive External Information for Event Detection Through Semantic Networks Word Representation and Attention Map. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12742. Springer, Cham. https://doi.org/10.1007/978-3-030-77961-0_56
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DOI: https://doi.org/10.1007/978-3-030-77961-0_56
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