Abstract
Event extraction (EE) is an essential yet challenging information extraction task, which aims at extracting event structures from unstructured text. Recent work on Chinese event extraction has achieved state-of-the-art performance by modeling events using the pre-trained model. However, the event type information has not been well utilized in existing event extraction methods. To address the issue, we propose the label semantic extension, which selects extension words according to the semantics of event type labels and adds them to the input sequence. Moreover, we propose \(p-n\ ETF\) values to measure the relationship between words and event types. Experiments on the ACE 2005 corpus show that our proposed method can significantly improve the performance of event extraction.
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Acknowledgment
This research is supported by “Pioneer” and “Leading Goose” R &D Program of Zhejiang (Grant No. 2022C03174), Fundamental Research Funds for the Provincial Universities of Zhejiang (NO. GK229909299001-023) and A Project Supported by Scientific Research Fund of Zhejiang Provincial Education Department (NO. Y202147115).
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Chen, Z. et al. (2022). Label Semantic Extension for Chinese Event Extraction. In: Lu, W., Huang, S., Hong, Y., Zhou, X. (eds) Natural Language Processing and Chinese Computing. NLPCC 2022. Lecture Notes in Computer Science(), vol 13551. Springer, Cham. https://doi.org/10.1007/978-3-031-17120-8_16
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DOI: https://doi.org/10.1007/978-3-031-17120-8_16
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