Abstract
Currently, many state-of-the-art event argument extraction systems are still based on an unrealistic assumption that gold-standard entity mentions are provided in advance. One popular solution of jointly extracting entities and events is to detect the entity mentions using sequence labeling approaches. However, this methods may ignore the syntactic relationship among triggers and arguments. We find that the constituents in the parse tree structure may help capture the internal relationship between words in an event argument. Besides, the predicate and the corresponding predicate arguments, which are mostly ignored in existing approaches, may provide more potential to represent the close relationship. In this paper, we address the event argument extraction problem in a more actual scene where the entity information is unavailable. Moreover, instead of using word-level sequence labeling approaches, we propose a shallow semantic parsing framework to extract event arguments with the event trigger as the predicate and the event arguments as the predicate arguments. In specific, we design and compare different features for the proposed model. The experimental results show that our approach advances state-of-the-arts with remarkable gains and achieves the best F1 score on the ACE 2005 dataset.
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This work was supported by the National Natural Science Foundation of China (No. 61602490).
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Luo, Z., Sui, G., Zhao, H., Li, X. (2019). A Shallow Semantic Parsing Framework for Event Argument Extraction. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11776. Springer, Cham. https://doi.org/10.1007/978-3-030-29563-9_9
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