Abstract
Event detection (ED) aims to identify the events in raw text. Most existing methods for ED include two steps: locating triggers and classifying them into correct event types. However, such methods require a lot of labor costs to annotate triggers and face the problems of word-trigger mismatch and polysemy, especially in Chinese. To address these challenges, we propose a novel method for ED based on event ontology and Siamese network. First, event ontology is introduced as priori event-based knowledge base, which provides enormous different event types with event class specification. And then, textual specifications of event class elements (such as actions, person, places and objects) could be obtained from event ontology, thus event detection is transformed into the problem of judging which event class specifications are similar to the raw text to be detected through the Siamese network, which solves the problem of high cost of trigger annotation and word-trigger mismatch. Extensive experiments with comprehensive analyses illustrate the effectiveness of proposed method. The source code of this paper can be obtained from https://github.com/nicahead/event_detection.
Supported by the National Key Research and Development Program of China (No. 2017YFE0117500), the National Natural Science Foundation of China (No. 61991410), the research project of the 54th Research Institute of China Electronics Technology Group (No. SKX192010019).
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Ni, C., Liu, W., Li, W., Wu, J., Ren, H. (2021). Chinese Event Detection Based on Event Ontology and Siamese Network. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management . KSEM 2021. Lecture Notes in Computer Science(), vol 12816. Springer, Cham. https://doi.org/10.1007/978-3-030-82147-0_36
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