基于BERT改进的文化活动事件论元抽取研究

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Abstract Event extraction methods usually use the small-scale open-domain event extraction corpus of ACE 2005, which is difficult for applying deep learning. A semi-supervised domain event argument extraction method is proposed to automatically annotate cultural event corpus from official websites of Chinese public libraries by using template and domain dictionary. Then manual annotation is applied to ensure the label accuracy. To resolve the problem of polysemy in word embedding layer, an improved method using BERT model and positional character embedding layer is proposed for the BiLSTM-CRF model. Experiments demonstrate an F₁ value of 84.9% for the proposed method of event argument extraction, which is superior to the classical event argument recognition methods.

Key words： NLP event extraction argument extraction pre-trained model deep learning

Received: 16 November 2020

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	Articles by authors
	LIN Zhi
	LI Yuan
	WANG Qinglin

Cite this article:

LIN Zhi,LI Yuan,WANG Qinglin. An Improved Argument Extraction Method for Cultural Events Based on BERT[J]. , 2022, 36(12): 115-122.

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http://jcip.cipsc.org.cn/EN/ OR http://jcip.cipsc.org.cn/EN/Y2022/V36/I12/115

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