基于平行交互注意力网络的中文电子病历实体及关系联合抽取

李丽双,王泽昊,秦雪洋,袁光辉

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PDF(1947 KB)
中文信息学报 ›› 2024, Vol. 38 ›› Issue (6) : 108-118.
信息抽取与文本挖掘

基于平行交互注意力网络的中文电子病历实体及关系联合抽取

  • 李丽双,王泽昊,秦雪洋,袁光辉
作者信息 +

Parallel Interactive Attention Network Based Joint Entity and Relation Extraction for Chinese Electronic Medical Record

  • LI Lishuang, WANG Zehao, QIN Xueyang, YUAN Guanghui
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摘要

基于电子病历构建医学知识图谱对医疗技术的发展具有重要意义,实体和关系抽取是构建知识图谱的关键技术。该文针对目前实体关系联合抽取中存在的特征交互不充分的问题,提出了一种平行交互注意力网络(PIAN)以充分挖掘实体与关系的相关性,在多个标准的医学和通用数据集上取得最优结果;当前中文医学实体及关系标注数据集较少,该文基于中文电子病历构建了实体和关系抽取数据集(CEMRIE),与医学专家共同制定了语料标注规范,并基于该文所提出的模型实验得出基准结果。

Abstract

The construction of medical knowledge graph based on electronic medical records is of great significance to the development of medical technology, where entity and relation extraction plays a pivotal role. In this paper, we propose a Parallel Interactive Attention Network (PIAN) which can fully exploit the correlation between entity and relation. Since there are few Chinese medical entity and relation annotation datasets, we construct an entity and relation extraction dataset based on Chinese electronic medical records (CEMRIE), formulate the corpus annotation specification with medical experts, and give the benchmark results based on our proposed model.

关键词

实体关系联合抽取 / 双向特征交互模块 / 自注意力机制 / 中文电子病历 / 数据集标注与构建

Key words

joint entity and relation extraction / bidirectional feature interaction module / self-attention mechanism / chinese electronic medical record / dataset annotation and construction

引用本文

导出引用
李丽双,王泽昊,秦雪洋,袁光辉. 基于平行交互注意力网络的中文电子病历实体及关系联合抽取. 中文信息学报. 2024, 38(6): 108-118
LI Lishuang, WANG Zehao, QIN Xueyang, YUAN Guanghui. Parallel Interactive Attention Network Based Joint Entity and Relation Extraction for Chinese Electronic Medical Record. Journal of Chinese Information Processing. 2024, 38(6): 108-118

参考文献

[1] LAN Y, HE S, LIU K, et al. Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion[J]. BMC Medical Informatics and Decision Making, 2021, 21: 1-12.
[2] ZHENG S, WANG F, BAO H, et al.Joint extraction of entities and relations based on a novel tagging scheme[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017: 1227-1236.
[3] WANG Y, YU B, ZHANG Y, et al. TPLinker: Single-stage joint extraction of entities and relations through token pair linking[C]//Proceedings of the 28th International Conference on Computational Linguistics, 2020: 1572-1582.
[4] REN F, ZHANG L, YIN S, et al. A novel global feature oriented relational triple extraction model based on table filling[C]//Proceedings of EMNLP, 2021: 2646-2656.
[5] WANG Y, SUN C, WU Y, et al. UniRE: A unified label space for entity relation extraction[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021: 220-231.
[6] MIWA M, BANSAL M. End-to-end relation extraction using LSTMs on sequences and tree structures[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016: 1105-1116.
[7] BEKOULIS G, DELEU J, DEMEESTER T, et al. Joint entity recognition and relation extraction as a multi-head selection problem[J]. Expert Systems with Applications, 2018, 114: 34-45.
[8] WEI Z, SU J, WANG Y, et al. A novel cascade binary tagging framework for relational triple extraction[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020: 1476-1488.
[9] WANG J, LU W. Two are better than one: Joint entity and relation extraction with table-sequence encoders[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2020: 1706-1721.
[10] YAN Z, ZHANG C, FU J, et al. A partition filter network for joint entity and relation extraction[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2021: 185-197.
[11] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 6000-6010.
[12] REI M, CRICHTON G, PYYSALO S. Attending to characters in neural sequence labeling models[C]//Proceedings of COLING, the 26th International Conference on Computational Linguistics, 2016: 309-318.
[13] LUO L, YANG Z, YANG P, et al. An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition[J]. Bioinformatics, 2018, 34(8): 1381-1388.
[14] LEE J, YOON W, KIM S, et al. BioBERT: A pre-trained biomedical language representation model for biomedical text mining[J]. Bioinformatics, 2020, 36(4): 1234-1240.
[15] ZENG D, LIU K, LAI S, et al. Relation classification via convolutional deep neural network[C]//Proceedings of COLING, the 25th International Conference on Computational Linguistics, 2014: 2335-2344.
[16] YI Z, LI S, YU J, et al. Drug-drug interaction extraction via recurrent neural network with multiple attention layers[C]//Proceedings of Advanced Data Mining and Applications: 13th International Conference, Singapore, 2017: 554-566.
[17] CHRISTOPOULOU F, TRAN T T, SAHU S K, et al. Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods[J]. Journal of the American Medical Informatics Association, 2020, 27(1): 39-46.
[18] SUN C, YANG Z, SU L, et al. Chemical-protein interaction extraction via Gaussian probability distribution and external biomedical knowledge[J]. Bioinformatics, 2020, 36(15): 4323-4330.
[19] SUI D, CHEN Y, ZHAO J, et al. Feded: Federated learning via ensemble distillation for medical relation extraction[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2020: 2118-2128.
[20] PARK C, PARK J, PARK S. AGCN: Attention-based graph convolutional networks for drug-drug interaction extraction[J]. Expert Systems with Applications, 2020, 159: 113538.
[21] ZHONG Z, CHEN D. A frustratingly easy approach for entity and relation extraction[C]//Proceedings of NAACL,2021: 50-61.
[22] LI F, ZHANG M, FU G, et al. A neural joint model for entity and relation extraction from biomedical text[J]. BMC Bioinformatics, 2017, 18(1): 1-11.
[23] LUO L, YANG Z, CAO M, et al. A neural network-based joint learning approach for biomedical entity and relation extraction from biomedical literature[J]. Journal of Biomedical Informatics, 2020,103: 103384.
[24] FEI H, ZHANG Y, REN Y, et al. A span-graph neural model for overlapping entity relation extraction in biomedical texts[J]. Bioinformatics, 2021, 37(11): 1581-1589.
[25] KENTON J D M W C, TOUTANOVA L K. BERT: Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of NAACL-HLT, 2019: 4171-4186.
[26] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
[27] LIU Y, OTT M, GOYAL N, et al. RoBERTa: A robustly optimized bert pretraining approach[J]. arXiv preprint arXiv: 1907.11692, 2019.
[28] DODDINGTON G R, MITCHELL A, PRZYBOCKI M A, et al. The automatic content extraction (ace) program-tasks, data, and evaluation[C]//Proceedings of the 4th International Conference on Language Resources and Evaluation., 2004: 837-840.
[29] ZHANG N, CHEN M, BI Z, et al. CBLUE: A Chinese biomedical language understanding evaluation benchmark[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022: 7888-7915.
[30] 王泽儒,柳先辉.基于指针级联标注的中文实体关系联合抽取模型[J].武汉大学学报(理学版),2022,68(03): 304-310.

基金

国家自然科学基金(62076048);大连市科技创新基金(2020JJ26GX035)
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