Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220200112-7.doi: 10.11896/jsjkx.220200112

• Artificial Intelligence • Previous Articles     Next Articles

Entity Relation Extraction Method in Weapon Field Based on DCNN and GLU

LI Han1, HOU Shoulu1, TONG Qiang1,2, CHEN Tongtong3, YANG Qimin1, LIU Xiulei1,2   

  1. 1 Laboratory of Data Science and Information Studies,Beijing Information Science and Technology University,Beijing 100101,China;
    2 Beijing Advanced Innovation Center for Materials Genome Engineering,Beijing Information Science and Technology University,Beijing 100101,China;
    3 Beijing Institute of Tracking and Telecommunications Technology,Beijing 100094,China
  • Online:2023-06-10 Published:2023-06-12
  • About author:LI Han,born in 1997,postgraduate.Her main research interests include know-ledge graph and relation extraction,and so on. LIU Xiulei,born in 1981,Ph.D,professor,is a member of China Computer Federation.His main research interests include ontology matching,semantic sensor,knowledge graph,semantic Web and semantic search,and so on.
  • Supported by:
    National Key R&D Program of China(2021YFB2600600),Promoting the Classified Development of Universities-Key Research and Cultivation Projects(2121YJPY225,2121YJPY226),Natural Science Foundation of Beijing Information Science & Technology University and Innovation Capacity Building of Scientific Research Institutions-Institute of Data Science and Information Analysis.

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Abstract: Unstructured text data in the field of weapons is usually very complex.In a single sentence,one weapon may be associated with multiple weapons or there may be multiple relations between two weapons.An entity relation extraction method based on dilated convolutional neural network and gated linear unit is proposed to solve the problem of overlapping relation in this type of data.This method introduces the sentence coding vector into the dilated convolutional neural network model with gated linear unit,which combines word vector and position vector.And it introduces the self-attention mechanism to extract the feature information of entities in sentences quickly.Through hierarchical sequence annotation,this model identifies all entities in the sentence and all relations and object entities corresponding to each subject entity,and generates the entity relation triplet in the field of weapons.The F1 value of this method on the self-labeled weapon field data set is 81.1%,and it has a certain entity relation extraction ability,according to the experimental results.The F1 value for various overlap types is greater than 78%,which solves the problem of unstructured data relation overlap.At the same time,it performs admirably on the NYT public data set.

Key words: Relation extraction, Overlapping relation, Dilated convolutional neural network, Gated linear unit

CLC Number: 

  • TP391
[1]ZENG X,ZENG D,HE S,et al.Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers).Melbourne,Australia:Association for Computational Linguistics,2018:506-514.
[2]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.Association for Computational Linguistics,2020:1476-1488.
[3]WANG Y,YU B,ZHANG Y,et al.TPLinker:Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking[J].arXiv:2010.13415,2020.
[4]XU J,ZHANG Z X,WU Z X.Review on Techniques of Entity Relation Extraction[J].New Technology of Library and Information Service,2008,24(8):18-23.
[5]CHEN H W,ZHANG X Y,WANG T.Research on Named Entity Recognition[J].Computer Science,2005,32(4):44-48.
[6]TRAN K M,BISK Y,VASWANI A,et al.Unsupervised Neural Hidden Markov Models[C]//Proceedings of the Workshop on Structured Prediction for NLP.2016:63-71.
[7]MA X,HOVY E.End-to-end Sequence Labeling via Bi-direc-tional LSTM-CNNs-CRF[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics(Vo-lume 1:Long Papers).Berlin,Association for Computational Linguistics,2016:1064-1074.
[8]LI X,SUN X,MENG Y,et al.Dice Loss for Data-imbalanced NLP Tasks[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.Association for Computational Linguistics,2020:465-476.
[9]ZHONG Z,CHEN D.A Frustratingly Easy Approach for Entity and Relation Extraction[C]//Proceedings of the 2021 Confe-rence of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.Association for Computational Linguistics,2021:50-61.
[10]LI X,FENG J,MENG Y,et al.A Unified MRC Framework for Named Entity Recognition[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.Association for Computational Linguistics,2020:5849-5859.
[11]WANG X,JIANG Y,BACH N,et al.Automated Concatenation of Embeddings for Structured Prediction[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing(Long Papers).Association for Computational Linguistics,2021:2643-2660.
[12]ZHOU W,CHEN M.Learning from Noisy Labels for Entity-Centric Information Extraction[C]//Proceedings of the 2021 Conference on Empirical Methods in Natural Language Proces-sing.Association for Computational Linguistics,2021:5381-5392.
[13]SCHWETER S,AKBIK A.FLERT:Document-Level Features for Named Entity Recognition[J].arXiv:2011.06993,2020.
[14]YE D,LIN Y,SUN M.Pack Together:Entity and Relation Extraction with Levitated Marker[J].arXiv:2109.06067,2021.
[15]E H H,ZHANG W J,XIAO S Q,et al.Survey of Entity Relationship Extraction Based on Deep Learning[J].Journal of Software,2019,30(6):1793-1818.
[16]SANTOS C N,XIANG B,ZHOU B.Classifying Relations byRanking with Convolutional Neural Networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing(Long Papers).Beijing:Association for Computational Linguistics,2015:626-634.
[17]WANG L,CAO Z,MELO G,et al.Relation Classification via Multi-Level Attention CNNs[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers).Berlin:Association for ComputationalLinguistics,2016:1298-1307.
[18]WANG H,TAN M,YU M,et al.Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers[C]//Proceedings of the 57th Annual Meeting of the Association for ComputationalLinguistics.Florence:Association for Computational Linguistics,2019:1371-1377.
[19]COHEN A D,ROSENMAN S,GOLDBERG Y.Relation Classification as Two-way Span-Prediction[J].arXiv:2010.04829,2020.
[20]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(Volume 1:Long Papers).Berlin:Association for Computational Linguistics,2016:1105-1116.
[21]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(Volume 1:Long Papers).Vancouver:Association for Computational Linguistics,2017:1227-1236.
[22]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.
[23]SUI D,CHEN Y,LIU K,et al.Joint Entity and Relation Extraction with Set Prediction Networks[J].arXiv:2011.01675,2020.
[24]HUGUET CABOT P L,NAVIGLI R.REBEL:Relation Extraction By End-to-end Language generation[C]//Findings of the Association for Computational Linguistics:EMNLP 2021.Punta Cana:Association for Computational Linguistics,2021:2370-2381.
[25]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient Estima-tion of Word Representations in Vector Space[J].arXiv:1301.3781,2013.
[26]VASWANI A,SHAZEER N,PARMAR N,et al.Attention isAll You Need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems.2017:6000-6010.
[27]RIEDEL S,YAO L,MCCALLUM A.Modeling Relations and Their Mentions without Labeled Text[C]//Machine Learning and Knowledge Discovery in Databases.Berlin:Springer,2010:148-163.
[28]FU T J,LI P H,MA W Y.GraphRel:Modeling Text as Relational Graphs for Joint Entity and Relation Extraction[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.Florence:Association for Computational Linguistics,2019:1409-1418.
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