research-article

Entity Pair Recognition using Semantic Enrichment and Adversarial Training for Chinese Drug Knowledge Extraction

Authors:

JinGuang GuAuthors Info & Claims

ISAIMS '21: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences

Pages 35 - 42

https://doi.org/10.1145/3500931.3500939

Published: 22 December 2021 Publication History

Abstract

Existing knowledge extraction methods in pharmacy often use natural language processing tools and deep learning model to identify drug entities and extract their relationships from drug instructions, thus obtaining drug-drug or drug-disease knowledge. However, sentences in drug instructions may contain multiple drug-related entities, and existing methods lack the capability of identifying valid the "drug-drug" or "drug-disease" entity pairs. This will introduce significant noise data in the subsequent tasks such as entity relationship extraction and knowledge graph construction. Meanwhile, some mentions in the sentence can have hierarchical relations even if they do not form valid entity pairs, such information is also crucial to knowledge extraction. To solve these two problems, this paper proposes an entity pair verification model based on entity semantic enhancement and adversarial training. Through the experiment on more than 2000 kinds of drug instructions data, the experimental results show that the F1 value of the model for entity pair verification is up to 98.65%, which is up to 9.37% compared with the existing methods.

References

[1]

Motlagh F G. DsOn: Ontology-Driven Model for Symptom and Drug Knowledge Extraction on Social Media[C]// 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM). 2020.

[2]

Lin Y, Shen S, Liu Z, et al. Neural Relation Extraction with Selective Attention over Instances[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.

[3]

Parsaeimehr E, Fartash M, Torkestani J A. An Enhanced Deep Neural Network-Based Architecture for Joint Extraction of Entity Mentions and Relations[J]. International Journal of Fuzzy Logic and Intelligent Systems, 2020, 20(1):69--76.

[4]

Zhong Z, Chen D. A Frustratingly Easy Approach for Joint Entity and Relation Extraction[J]. 2020.

[5]

YANG Z H, LIN H F, LI Y P.Exploiting the performance of dictionary-based bio-entity name recognition in biomedical literature[J].Computational biology and chemistry, 2008, 32(4):287--291.

[6]

WEI C H, PENG Y F, LEAMAN R, et al.Overview of the Bio Creative V chemical disease relation (CDR) task[C]//Proceedings of the 5th Bio Creative Challenge Evaluation Workshop.Oxford:Oxford University Press, 2015:154--166.

[7]

Gong lejun, Liu Xiaolin, Gao Zhihong, Li Huakang. Extraction of drug interaction relationship based on two-way Gru and CNN [J / OL]. Journal of Shaanxi Normal University (NATURAL SCIENCE EDITION): 1--6 [2020-11-25] http://kns.cnki.net/kcms/detail/61.1071.N.20201013.1330.002.html.

[8]

Wang Yongchao, Mu Hualing, Zhou Lingzhi, Xing Wei. Joint entity and relationship extraction method based on pointer network [J/OL]. Computer application research: 1--5 [2020-11-28] https://doi.org/10.19734/j.issn.1001-3695.2020.04.0113.

[9]

Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]//Advances in neural information processing systems. 2017: 5998--6008.

[10]

Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[J]. 2018.

[11]

Szegedy C, Zaremba W, Sutskever I, et al. Intriguing properties of neural networks[J]. Computer Science, 2013.

[12]

Goodfellow I J, Shlens J, Szegedy C. Explaining and harnessing adversarial examples[J]. arXiv preprint arXiv:1412.6572, 2014.

[13]

I Staliūnait, Gorinski P J, Iacobacci I. Improving Commonsense Causal Reasoning by Adversarial Training and Data Augmentation. 2021.

[14]

Cui Y, Che W, Liu T, et al. Pre-training with whole word masking for Chinese BERT [EB/OL]. [2019-10-29]. https://arxiv.org/pdf/1906.08101.pdf.

[15]

Su Jianlin. (Mar. 01, 2020). On confrontation training: significance, methods and thinking (with keras Implementation) [blog post]. Retrieved from https://spaces.ac.cn/archives/7234.

[16]

Luong M T, Pham H, Manning C D. Effective Approaches to Attention-based Neural Machine Translation[J]. Computer ence, 2015.

[17]

Johnson R, Zhang T. Deep Pyramid Convolutional Neural Networks for Text Categorization[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017.

[18]

K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770--778.

[19]

Choi H, Kim J, Joe S, et al. Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks[J]. 2021.

[20]

Lu Y, Yang R, Jiang X, et al. Research on Military Event Detection Method Based on BERT-BiGRU-Attention[C]// 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE). IEEE, 2021.

[21]

Liu Y, Ott M, Goyal N, et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach[J]. 2019.

[22]

Giorgi J, Nitski O, Wang B, et al. DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations[C]// Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Cited By

Gao FSong XGu JZhang LLiu YZhang XLiu YJing S(2023)Fine-Grained Relation Extraction for Drug Instructions Using Contrastive Entity EnhancementIEEE Access10.1109/ACCESS.2023.327928811(51777-51788)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3279288
Gao FZhou LJing SWang ZWang YMiao SZhang XGuo JShan TLiu YZhang X(2022)Fine-Grained Drug Interaction Extraction Based on Entity Pair Calibration and Pre-Training Model for Chinese Drug InstructionsInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.30790818:1(1-23)Online publication date: 12-Aug-2022
https://dl.acm.org/doi/10.4018/IJSWIS.307908

Index Terms

Entity Pair Recognition using Semantic Enrichment and Adversarial Training for Chinese Drug Knowledge Extraction
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Information extraction

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ISAIMS '21: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences

October 2021

593 pages

ISBN:9781450395588

DOI:10.1145/3500931

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 December 2021

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Key Laboratory of Rich Media Digital Publishing, Content Organization and Knowledge Service
National Natural Science Foundation of China

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ISAIMS 2021

ISAIMS 2021: 2nd International Symposium on Artificial Intelligence for Medicine Sciences

October 29 - 31, 2021

Beijing, China

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Overall Acceptance Rate 53 of 112 submissions, 47%

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Cited By

Gao FSong XGu JZhang LLiu YZhang XLiu YJing S(2023)Fine-Grained Relation Extraction for Drug Instructions Using Contrastive Entity EnhancementIEEE Access10.1109/ACCESS.2023.327928811(51777-51788)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3279288
Gao FZhou LJing SWang ZWang YMiao SZhang XGuo JShan TLiu YZhang X(2022)Fine-Grained Drug Interaction Extraction Based on Entity Pair Calibration and Pre-Training Model for Chinese Drug InstructionsInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.30790818:1(1-23)Online publication date: 12-Aug-2022
https://dl.acm.org/doi/10.4018/IJSWIS.307908

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