Utilizing BERT for entity relationship extraction in Chinese medical texts

Research Article
Open access

Utilizing BERT for entity relationship extraction in Chinese medical texts

Yaqian Ren 1*
  • 1 Central South University    
  • *corresponding author renyaqian@stu.hebmu.edu.cn
Published on 4 February 2024 | https://doi.org/10.54254/2755-2721/35/20230398
ACE Vol.35
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-295-4
ISBN (Online): 978-1-83558-296-1

Abstract

The Chinese medical sector has been somewhat lacking in knowledge graphs, a deficiency this study aims to address. By leveraging the prowess of the BERT pre-training model, a two-tier approach has been innovated that utilizes separate pre-trained encoders for both entity and relational models. These models are intricately linked: the output from the entity model seamlessly flows into the relational one, making it possible to adeptly extract entity relationships from Chinese medical texts. This research is anchored in the CMeIE dataset, sourced from the esteemed CHIP (China Health Information Processing) conference. This dataset stands as a recognized benchmark in evaluating Chinese medical texts. By harnessing this data, the methods have been rigorously tested and validated. The promising experimental results underscore the effectiveness of the approach in distilling relationships from Chinese medical literature. The implications of this research are profound. Beyond just enriching the Chinese medical domain, the boundaries of NLP technology are also being pushed. Potential applications are manifold: from constructing comprehensive Chinese medical knowledge graphs to assisting in early-stage medical diagnoses. This innovative approach not only addresses an existing gap but also sets the stage for future advancements in medical NLP.

Keywords:

pre-trained model, BERT, chinese medical text, entity relationship extraction

Ren,Y. (2024). Utilizing BERT for entity relationship extraction in Chinese medical texts. Applied and Computational Engineering,35,229-233.
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References

[1]. Ratinov, L., & Roth, D. (2009). Design challenges and misconceptions in named entity recognition. Computational Natural Language Learning (CoNLL), 147–155.

[2]. Chan, Y. S., & Roth, D. (2011). Exploiting syntactico-semantic structures for relation extraction. Association for Computational Linguistics: Human Language Technologies (ACL-HLT), 551–560.

[3]. Luan, Y., Wadden, D., He, L., Shah, A., Ostendorf, M., & Hajishirzi, H. (2019). A general framework for information extraction using dynamic span graphs. North American Chapter of the Association for Computational Linguistics (NAACL), 3036–3046.

[4]. Miwa, M., & Bansal, M. (2016). End-to-end relation extraction using LSTMs on sequences and tree structures. Association for Computational Linguistics (ACL), 1105–1116.

[5]. Li, Q., & Ji, H. (2014). Incremental joint extraction of entity mentions and relations. Association for Computational Linguistics (ACL), 402–412.

[6]. Wang, J., & Lu, W. (2020). Two are better than one: Joint entity and relation extraction with tablesequence encoders. Empirical Methods in Natural Language Processing (EMNLP).

[7]. Sun, C., Gong, Y., Wu, Y., Gong, M., Jiang, D., Lan, M., Sun, S., & Duan, N. (2019). Joint type inference on entities and relations via graph convolutional networks. Association for Computational Linguistics (ACL), 1361–1370.

[8]. Shang, Y., Huang, H., & Mao, X. -L. (2022). Onerel: Joint entity and relation extraction with one module in one step. CoRR, abs /2203.05412.

[9]. Sui, D., Chen, Y., Liu, K., Zhao, J., & Zeng, X. (2023). Joint Entity and Relation Extraction With Set Prediction Networks. IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2023.3264735.

[10]. Bai, T., Guan, H., Wang, S., Wang, Y., & Huang, L. (2021). Traditional Chinese medicine entity relation extraction based on CNN with segment attention. Neural Computing and Applications, 34(4), 2739–2748.

Cite this article

Ren,Y. (2024). Utilizing BERT for entity relationship extraction in Chinese medical texts. Applied and Computational Engineering,35,229-233.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-295-4(Print) / 978-1-83558-296-1(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.35
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Ratinov, L., & Roth, D. (2009). Design challenges and misconceptions in named entity recognition. Computational Natural Language Learning (CoNLL), 147–155.

[2]. Chan, Y. S., & Roth, D. (2011). Exploiting syntactico-semantic structures for relation extraction. Association for Computational Linguistics: Human Language Technologies (ACL-HLT), 551–560.

[3]. Luan, Y., Wadden, D., He, L., Shah, A., Ostendorf, M., & Hajishirzi, H. (2019). A general framework for information extraction using dynamic span graphs. North American Chapter of the Association for Computational Linguistics (NAACL), 3036–3046.

[4]. Miwa, M., & Bansal, M. (2016). End-to-end relation extraction using LSTMs on sequences and tree structures. Association for Computational Linguistics (ACL), 1105–1116.

[5]. Li, Q., & Ji, H. (2014). Incremental joint extraction of entity mentions and relations. Association for Computational Linguistics (ACL), 402–412.

[6]. Wang, J., & Lu, W. (2020). Two are better than one: Joint entity and relation extraction with tablesequence encoders. Empirical Methods in Natural Language Processing (EMNLP).

[7]. Sun, C., Gong, Y., Wu, Y., Gong, M., Jiang, D., Lan, M., Sun, S., & Duan, N. (2019). Joint type inference on entities and relations via graph convolutional networks. Association for Computational Linguistics (ACL), 1361–1370.

[8]. Shang, Y., Huang, H., & Mao, X. -L. (2022). Onerel: Joint entity and relation extraction with one module in one step. CoRR, abs /2203.05412.

[9]. Sui, D., Chen, Y., Liu, K., Zhao, J., & Zeng, X. (2023). Joint Entity and Relation Extraction With Set Prediction Networks. IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2023.3264735.

[10]. Bai, T., Guan, H., Wang, S., Wang, Y., & Huang, L. (2021). Traditional Chinese medicine entity relation extraction based on CNN with segment attention. Neural Computing and Applications, 34(4), 2739–2748.

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