Abstract
Biomedical trigger word identification is a challenging task in biomedical text mining, which plays a key role in improving biomedical research and disease prevention. The traditional trigger word identification methods rely too much on the establishment of dictionaries or rules, which lead to poor performance. Aiming at the above problems, this paper puts forward a BERT-CRF model. In this model, BERT is used to train the emission matrix in CRF model. By using the transformer architecture of BERT, the language model, is used to train. By using CRF layer, some constraints can be added to ensure that the final prediction results are valid. The experimental results on MLEE data set show that the proposed model achieves state-of-the-art performance with an F value of 82.71%, which indicates that this method is beneficial to improve the trigger word recognition performance.
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Acknowledgments
This work is supported by the National Science Foundation of China (No. 62006108), General program of China Postdoctoral Science Foundation (No. 2022M710593), Liaoning Provincial Science and Technology Fund project (No. 2021-BS-201), and Natural Science research projects of Liaoning Education Department (No. LQ2020027).
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He, X. et al. (2022). A Biomedical Trigger Word Identification Method Based on BERT and CRF. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_34
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DOI: https://doi.org/10.1007/978-3-031-20309-1_34
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