JACIII Vol.27 No.3 pp. 421-430
doi: 10.20965/jaciii.2023.p0421

Research Paper:

Causality Extraction Cascade Model Based on Dual Labeling

Fengxiao Yan ORCID Icon, Bo Shen ORCID Icon, and Chenyang Dai

Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University
3 Shangyuancun, Haidian District, Beijing 100044, China

Corresponding author

June 1, 2022
January 17, 2023
May 20, 2023
causality extraction, named entity recognition, BiLSTM, ACNN

Causal relation extraction is a crucial task in natural language processing. Current extraction methods have problems, including low accuracy of causal-event division and incorrect extraction of important semantic features. This study uses the bidirectional long short-term memory (BiLSTM) and attentive convolutional neural network (ACNN) models to construct a cascaded causal relationship extraction model to improve the precision of the extraction. The model uses two kinds of labels and then divides the causal event boundary after determining the relationship between the front and rear causal events. It automatically learns semantic features from sentences, reducing the dependence on external knowledge and improving the precision of extraction. The experimental results demonstrate that the precision of causality extraction can reach 81.67% and the F1 value can reach 83.2%.

Cite this article as:
F. Yan, B. Shen, and C. Dai, “Causality Extraction Cascade Model Based on Dual Labeling,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.3, pp. 421-430, 2023.
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