Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (10): 351-361.doi: 10.11896/jsjkx.230800111

• Artificial Intelligence • Previous Articles     Next Articles

Chemical-induced Disease Relation Extraction:Graph Reasoning Method Based on Evidence Focusing

ZHOU Xueyang1,2, FU Qiming1,2, CHEN Jianping2,3, LU You1,2, WANG Yunzhe1,2   

  1. 1 Electronic and Information Engineering,Suzhou University of Science and Technology,Suzhou,Jiangsu 215009,China
    2 Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency,Suzhou University of Science and Technology,Suzhou,Jiangsu 215009,China
    3 School of Architecture and Urban Planning,Suzhou University of Science and Technology,Suzhou,Jiangsu 215009,China
  • Received:2023-08-17 Revised:2024-01-15 Online:2024-10-15 Published:2024-10-11
  • About author:ZHOU Xueyang,born in 1998,postgra-duate.His main research interests include natural language processing and biomedical information mining.
    FU Qiming,born in 1985,Ph.D,professor,is a member of CCF(No.23956M).His main research interests include reinforcement learning,deep learning and intelligent information processing.
  • Supported by:
    National Key R&D Program of China(2020YFC2006602),National Natural Science Foundation of China(62102278,62072324),University Natural Science Foundation of Jiangsu Province(21KJA520005),Primary Research and Development Plan of Jiangsu Province(BE2020026),Postgraduate Education Reform Project of Jiangsu Province and Postgraduate Research & Practice Innovation Program of Jiangsu Province(KYCX23_3321).

PDF (PC)

236

Abstract: To address the problem of existing methods focusing too much on global information while neglecting a small amount of evidence clues and local mention interactions when mining the interaction between chemicals and diseases,a mention level document-level relation extraction method based on evidence focusing(EF-MUnet) is proposed.This method first models mention features based on context aware strategies and captures local interactions between adjacent mentions using two-dimensional convolution network.Secondly,to avoid irrelevant context interference,two evidence focusing strategies ATT-EF and RL-EF are proposed.The former uses similarity as a measure of evidence clues,while the latter uses reinforcement learning to unsupervised learn the optimal evidence extraction policy with the help of delayed feedback.Finally,U-net networks are used to capture global features at the entity level and fully explore semantic relationships.Experimental results show that compared with existing me-thods,EF-MUnet's F1 score improves by 9.7% on the biomedical dataset CDR,and it has more advantages in extracting inter-sentence relations.In addition,EF-MUnet achieves the highest accuracy of 98.6% on the dataset DMI for extracting interactions between drug and mutation,proving that it is an effective biomedical relation extraction method with good generalization ability.

Key words: Relation extraction, Evidence focusing, Reinforcement learning, Self-attention mechanism, Biomedicine

CLC Number: 

  • TP391.1
[1]HUANG Q,ZHU S,FENG Y,et al.Three sentences are all you need:Local path enhanced document relation extraction[C]//59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing.2021:998-1004.
[2]MA Y,WANG A,OKAZAKI N.DREEAM:Guiding Attention with Evidence for Improving Document-Level Relation Extraction[J].arXiv:2302.08675,2023.
[3]HUANG K,QI P,WANG G,et al.Entity and evidence guided document-level relation extraction[C]//Proceedings of the 6th Workshop on Representation Learning for NLP(RepL4NLP-2021).2021:307-315.
[4]LI J,SUN Y,JOHNSON R J,et al.BioCreative V CDR task corpus:a resource for chemical disease relation extraction [J].Database,2016;baw068.
[5]YE W,LUO R B,HENRY C M L,et al.Renet:A deep learning approach for extracting gene-disease associations from literature[C]//International Conference on Research in Computational Molecular Biology.Springer,2019:272-284.
[6]YAO Y,YE D M,LI P,et al.DocRED:A large-scale document-level relation extraction dataset[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.2019:764-777.
[7]SON S H,LEE N R,GEE M S,et al.Chemical Knockdown of Phosphorylated p38 Mitogen-Activated Protein Kinase(MAPK) as a Novel Approach for the Treatment of Alzheimer's Disease [J].ACS Central Science,2023,9(3):417-426.
[8]THAISRIVONGS D A,MORRIS,W J,SCOTT J D.Discovery and Chemical Development of Verubecestat,a BACE1 Inhibitor for the Treatment of Alzheimer's Disease [J].ACS Symposium Series,2018,1037:53-89.
[9]HUANG R G,LI X B,WANG Y Y,et al.Endocrine-disruptingchemicals and autoimmune diseases [J].Environmental Research,2023,231:116222.
[10]LOWE D M,O'BOYLE N M,SAYLE R A.Efficient chemical-disease identification and relationship extraction using Wikipedia to improve recall [J].Database,2016,2016:baw039.
[11]LI B,YE W,SHENG Z,et al.Graph enhanced dual attention network for document-level relation extraction[C]//28th International Conference on Computational Linguistics.2020:1551-1560.
[12]ZHANG Z,YU B,SHU X,et al.Document-level relation extraction with dual-tier heterogeneous graph[C]//28th International Conference on Computational Linguistics.2020:1630-1641.
[13]WANG D,HU W,CAO E,et al.Global-to-local neural networks for document-level relation extraction[C]//2020 Conference on Empirical Methods in Natural Language Processing.2020:3711-3721.
[14]ZENG S,XU R,CHANG B,et al.Double graph based reasoning for document-level relation extraction[C]//2020 Conference on Empirical Methods in Natural Language Processing,Proceedings of the Conference.2020:1630-1640.
[15]WANG X,KEHAI C,TIEJUN Z.Document-level relation extraction with reconstruction[C]//35th AAAI Conference on Artificial Intelligence.2021:14167-14175.
[16]NAN G,GUO Z,SEKULIĆ I,et al.Reasoning with latent struc-ture refinement for document-level relation extraction[C]//58th Annual Meeting of the Proceedings of the Conference. 2020:1546-1557.
[17]YE D,LIN Y,DU J,et al.Coreferential reasoning learning for language representation[C]//2020 Conference on Empirical Methods in Natural Language Processing. 2020:7170-7186.
[18]XU B,WANG Q,LYU Y,et al.Entity Structure Within and Throughout:Modeling Mention Dependencies for Document-Level Relation Extraction[C]//35th AAAI Conference on Artificial Intelligence.2021:14149-14157.
[19]HONG W,CHRISTFRIED F,ROB S,et al.Fine-tune Bert forDocRED with Two-step Process[J].arXiv:1909.11898,2019.
[20]ZHOU W,HUANG K,MA T,et al.Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling[C]//35th AAAI Conference on Artificial Intelligence.2021:14612-14620.
[21]ZHANG N Y,CHEN X XIE X,et al.Document-level relation extraction as semantic segmentation[C]//Proceedings of the 30th International Joint Conference on Artificial Intelligence.2021:3999-4006.
[22]ZHANG L,CHENG Y.A Densely Connected Criss-Cross Attention Network for Document-level Relation Extraction[J].arXiv:2203.13953,2022.
[23]XU W,CHEN K,MOU L,et al.Document-Level Relation Extraction with Sentences Importance Estimation and Focusing[C]//2022 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. 2022:2920-2929.
[24]VASWANI A,SHAZEER N,PARMAR N,et al.Attention isall you need[C]//Advances in Neural Information Processing Systems 30－Proceedings of the 2017 Conference.Long Beach,CA,United states,2017:5999-6009.
[25]SUTTON R S,BARTO A G.Reinforcement learning:An introduction [M].MIT press,2018.
[26]SUTTON R S,MCALLESTER D,SINGH S,et al.Policy gra-dient methods for reinforcement learning with function approximation[C]//Advances in Neural Information Processing Systems.1999.
[27]RIDNIK T,BEN-BARUCH E,ZAMIR,et al.Asymmetric loss for multi-label classification[C]//18th IEEE/CVF International Conference on Computer Vision.2021:82-91.
[28]GU J,QIAN L,ZHOU G.Chemical-induced disease relation extraction with various linguistic features [J].Database,2016,2016:baw042.
[29]PENG N,POON H,QUIRK C,et al.Cross-sentence n-ary relation extraction with graph lstms [J].Transactions of the Association for Computational Linguistics,2017,5:101-115.
[30]BELTAGY I,LO K,COHAN A.SciBERT:A pretrained language model for scientific text[C]//2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing.2019:3615-3620.
[31]VERGA P,STRUBELL E,MCCALLUM A.Simultaneouslyself-attending to all mentions for full-abstract biological relation extraction[C]//2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2018:872-884.
[32]ZHENG W,LIN H F,LI Z H,et al.An effective neural model extracting document level chemical-induced disease relations from biomedical literature [J].Journal of Biomedical Informa-tics,2018,83(2018):1-9.
[33]CHRISTOPOULOU F,MIWA M,ANANIADOU S.Connec-ting the dots:Document-level neural relation extraction with edge-oriented graphs[C]//2019 Conference on Empirical Me-thods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing.2019:4925-4936.
[34]TRAN H M,NGUYEN M T,NGUYEN T H.The dots have their values:exploiting the node-edge connections in graph-based neural models for document-level relation extraction[C]//ACL 2020:EMNLP 2020:Findings of the Association for Computational Linguistics.2020:4561-4567.
[35]LAI P T,LU Z.BERT-GT:cross-sentence n-ary relation extraction with BERT and Graph Transformer [J].Bioinformatics,2020,36(24):5678-5685.
[36]LI J,XU K,LI F,et al.MRN:A locally and globally mention-based reasoning network for document-level relation extraction[C]//ACL-IJCNLP 2021:Findings of the Association for Computational Linguistics.2021:1359-1370.
[37]LI Z G,LIN H F,SHEN C,et al.Document-level Chemical-induced Disease Relation Extraction via Cross Self-attention [J].Journal of Chinese Information Processing,2022,36(7):98-105.
[38]GIORGI J,BADER G D,WANG B.A sequence-to-sequence ap-proach for document-level relation extraction[C]//BioNLP 2022 @ACL 2022:Proceedings of the 21st Workshop on Biomedical Language Processing.2022:10-25.
[39]DUAN J Y,YANG X,WANG H,et al.Document level relationship extraction based on inter sentence information in graph attention convolutional networks [J].Computer Science,2023,50(S1):191-196.
[40]DONG Y,XU X.Relational distance and document-level con-trastive pre-training based relation extraction model [J].Pattern Recognition Letters,2023,167:132-140.
[41]WANG N,CHEN T,REN C,et al.Document-level relation extraction with multi-layer heterogeneous graph attention network [J].Engineering Applications of Artificial Intelligence,2023,123:106212.
[42]GUO Z,ZHANG Y,LU W.Attention guided graph convolu-tional networks for relation extraction[C]//57th Annual Mee-ting of the Association for Computational Linguistics.2019:241-251.
[43]DEVLIN J,CHANG M W,LEE K,et al.Bert:pre-training of deep bidirectional transformers for language understanding[C]//2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2019:4171-4186.
[44]PENG Y,YAN S,LU Z.Transfer learning in biomedical natural language processing:an evaluation of BERT and ELMo on ten benchmarking datasets[C]//18th SIGBioMed Workshop on Biomedical Natural Language Processing.2019:58-65.
[45]JIANG Y,ZHOU Y,TU K.Learning and evaluation of latent dependency forest models [J].Neural Computing and Applications,2019,31:6795-6805.
[46]ZHAO L,XU W,GAO S,et al.Cross-sentence N-ary re-lation classification using LSTMs on graph and sequence structures [J].Knowledge-Based Systems,2020,207:106266.
[47]ZHAO D,WANG J,LIN H,et al.Biomedical cross-sentence relation extraction via multihead attention and graph convolutional networks [J].Applied Soft Computing,2021,104:107230.
[48]LAI P T,LU Z.BERT-GT:cross-sentence n-ary relation extraction with BERT and Graph Transformer [J].Bioinformatics,2020,36(24):5678-5685.
[49]CHEN X,ZHANG M,XIONG S,et al.On the form of parsed sentences for relation extraction [J].Knowledge-Based Systems,2022,251:109184.
[1] YAN Xin, HUANG Zhiqiu, SHI Fan, XU Heng. Study on Following Car Model with Different Driving Styles Based on Proximal PolicyOptimization Algorithm [J]. Computer Science, 2024, 51(9): 223-232.
[2] WANG Tianjiu, LIU Quan, WU Lan. Offline Reinforcement Learning Algorithm for Conservative Q-learning Based on Uncertainty Weight [J]. Computer Science, 2024, 51(9): 265-272.
[3] ZHOU Wenhui, PENG Qinghua, XIE Lei. Study on Adaptive Cloud-Edge Collaborative Scheduling Methods for Multi-object State Perception [J]. Computer Science, 2024, 51(9): 319-330.
[4] LI Jingwen, YE Qi, RUAN Tong, LIN Yupian, XUE Wandong. Semi-supervised Text Style Transfer Method Based on Multi-reward Reinforcement Learning [J]. Computer Science, 2024, 51(8): 263-271.
[5] ZHANG Lu, DUAN Youxiang, LIU Juan, LU Yuxi. Chinese Geological Entity Relation Extraction Based on RoBERTa and Weighted Graph Convolutional Networks [J]. Computer Science, 2024, 51(8): 297-303.
[6] LI Jiaying, LIANG Yudong, LI Shaoji, ZHANG Kunpeng, ZHANG Chao. Study on Algorithm of Depth Image Super-resolution Guided by High-frequency Information ofColor Images [J]. Computer Science, 2024, 51(7): 197-205.
[7] WANG Xianwei, FENG Xiang, YU Huiqun. Multi-agent Cooperative Algorithm for Obstacle Clearance Based on Deep Deterministic PolicyGradient and Attention Critic [J]. Computer Science, 2024, 51(7): 319-326.
[8] GAO Yuzhao, NIE Yiming. Survey of Multi-agent Deep Reinforcement Learning Based on Value Function Factorization [J]. Computer Science, 2024, 51(6A): 230300170-9.
[9] ZHONG Yuang, YUAN Weiwei, GUAN Donghai. Weighted Double Q-Learning Algorithm Based on Softmax [J]. Computer Science, 2024, 51(6A): 230600235-5.
[10] LIU Xiaohu, CHEN Defu, LI Jun, ZHOU Xuwen, HU Shan, ZHOU Hao. Speaker Verification Network Based on Multi-scale Convolutional Encoder [J]. Computer Science, 2024, 51(6A): 230700083-6.
[11] JIANG Zhihan, ZAN Hongying, ZHANG Li. Application of Subject Enhanced Cascade Binary Pointer Tagging Framework in Chinese Medical Entity and Relation Extraction [J]. Computer Science, 2024, 51(6A): 230800179-6.
[12] AN Xiankua, XIAO Rong, YANG Xiao. Document-level Relation Extraction Integrating Evidence Sentence Extraction [J]. Computer Science, 2024, 51(6A): 230800081-6.
[13] PENG Bo, LI Yaodong, GONG Xianfu, LI Hao. Method for Entity Relation Extraction Based on Heterogeneous Graph Neural Networks and TextSemantic Enhancement [J]. Computer Science, 2024, 51(6A): 230700071-5.
[14] QUE Yue, GAN Menghan, LIU Zhiwei. Object Detection with Receptive Field Expansion and Multi-branch Aggregation [J]. Computer Science, 2024, 51(6A): 230600151-6.
[15] LI Danyang, WU Liangji, LIU Hui, JIANG Jingqing. Deep Reinforcement Learning Based Thermal Awareness Energy Consumption OptimizationMethod for Data Centers [J]. Computer Science, 2024, 51(6A): 230500109-8.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.