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An Entity Relation Extraction Algorithm Incorporating Multi-Attention Mechanisms and Remote Supervision

Published: 21 December 2023 Publication History

Abstract

Entity-relation extraction is one of the important tasks in information extraction, which is widely used in natural language processing, construction of knowledge graph, and information reasoning work. Remote supervised algorithms utilize heuristic alignment of knowledge base with corpus, which can generate large-scale labeled corpus resources without human involvement. For the problem that noise is introduced into remote supervised algorithms leading to a decrease in the recognition rate, this paper introduces a multi-level attention mechanism into remote supervised learning algorithms, using word attention to strengthen the role of key words related to entity relations, and using sentence attention to further emphasize the role of key sentences; for the problem that entity pairs may store more than one relation, relational attention is invoked to find a relation in sentence packages that have the same entity pairs The exact expression of the relationship is found in the package of sentences with the same entity pairs. Experiments are conducted on the Freebase+NYT corpus. The results indicate that the algorithm designed in this paper is significantly improved compared to the classical algorithms.

References

[1]
LIU S Y, LI B C, GUO Z Z.et al. Revie w of Entity Relation Extraction[J]. Journal of Information Engineering University, 2016.17(5):541-547.
[2]
MINTZ M. STEVEN B. RION S.et al. Distant super vision for relation extraction without labeled data [C] // Proceedings of Joint Conference of the Meeting of the ACL. Stroudsburg: As sociation for Computational Linguistics,2009: 1003-1011.
[3]
GAN L X, WAN C X, LIU D X, Chinese Named Entity Relation Extraction Based on Syntactic and Semantic Features[J] Journal of Computer Research and Development, 2016,53(2):284-302.
[4]
CHOI S P, LEE S, JUNG H, An intensive case study on kernel based relation extraction[J]. Multimedia Tools & Applications. 2014.71(2) :741-767.
[5]
ZHAO X, LI X, ZHANG Z H, Community discovery algorithm combining community embedding and node embedding [J]. Computer Science, 2020, 47(12):279-284.
[6]
CAI H, ZHENG V, CHANG K. A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(9):1616-1637.
[7]
ZENG D, LIU K, LAI S, Relation Classification via Convolutional Deep Neural Network[C] //Proceedings of the 25th International Conference on Computational Linguistics. 2014:2335-2344.
[8]
[WANG D, CUI P, ZHU W. Structural Deep Network Embedding [C]∥Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM, 2016: 1225-1234.
[9]
LIF L, KE J. Research Progress of Entity Relation Extraction Base on Deep Learning Framework[J] Information Science. 2018.V36(3) :169-176.
[10]
HOFFMANN R, ZHANG C, LING X, Knowledge based weak supervision for information extraction of overlapping relations[C] // Proceedings of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics. 2011:541- 550.
[11]
SURDEANU M, TIBSHIRANI J, NALLAPATI R, Multi-instance multi-label learning for relation extraction[C] // Proceedings of Joint Conference on Empirical Methods in Natural Language Processing and Computational Natura Language Learning.2012 :455 -465.
[12]
ZHOU L E, YOU J G. Social Recommendation with Embedding of Summarized Graphs[J]. Journal of Chinese Computer Systems, 2021, 42(1): 78-84.
[13]
GROVER A, LESKOVEC J. Node2vec: Scalable Feature Learning for Networks[C]∥Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM, 2016: 855-864.
[14]
BENJAMIN R, DIETRICH K. Combining Generative and Discriminative Model Scores for Distant Supervision[C] // Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 2013:24-29.
[15]
ZENG D, LIU K. CHEN Y, Distant supervision for relation extraction via piecewise convolutional neural networks[C]// Proceedings of Conference on Empirical Methods in Natural Language Processing. 2015:1753-1762.
[16]
JIANG X. WANG Q.LI P.et al Relation Extraction with Multi-instance Multi-label Convolutional Neural Networks[C] // 26th International Conference on Computational Linguistics. 2016:1471-1480.
[17]
LEI K. CHEN D, LI Y, Cooperative Denoising for Distantly Supervised Relation Extraction [C]// Proceedings of the 27th International Conference on Computational Linguistics. 2018:426- 436.
[18]
BAHDANAU D, CHO K, BENGIO Y, Neural Machine Translation by Jointly Learning to Align and Translate[J]. International conference on learning representations .2015.12(7):366- 381.
[19]
LIN Y, SHEN S, LIU Z, Neural relation extraction with selective attention over instances[C] // Proceedings of Meeting of the Association for Computational Linguistics. 2016: 2124-2133.
[20]
JIG, LIU K. HE s, Distant Supervision for Relation Extraction with Sentence-level Attention and Entity Descriptions[C]// Proceedings of the Thirty First AAAI Conference on Artificial Intelligence. 2017 :3060- -3066.
[21]
HE D, ZHANG H, HAO W, A Customized Attention Based Long Short-Term Memory Network for Distant Supervised Relation Extraction[J]. Neural Computation, 2017 .27(7):1964-1985.
[22]
PENNINGTON J, SOCHER R, MANNING C. Glove: Global vectors for word representation[C] // Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 2014:15321543.
[23]
MIKOLOV T, CHEN K. CORRADOG, Efficient Estimation of Word Representations in Vector Space [OL] https://anxiv. org/abs/1301.3781.
[24]
CHUNG J. CULCEHRE C, CHO K.et al Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling[J]. arXiv:1412. 3555.2014.
[25]
HINTON G E, SRIVASTAVA N, KRIZHEVSKY A.et al Improving neural networks by preventing co- adaptation of feature detectors[J]. Computer Science, 2012.3(4):212-223.
[26]
RIEDEL S, YAO L. MOCALLUM A.et al Modeling relations and their mentions without labeled text[C] // European Conference on Machine Lea rning.2010:148-163.
[27]
WANG C, PAN S, LONG G, MGAE: Marginalized Graph Autoencoder for Graph Clustering[C]∥International Conference on Information and Knowledge Management. Singapore: ACM, 2017: 889-898.
[28]
XIONG F, WANG X, PAN S, Social Recommendation with Evolutionary Opinion Dynamics[J]. IEEE Transactions on Systems Man Cybernetics-Systems, 2018, 50(10):3804-3816.
[29]
SHI C, HU B, ZHAO W, Heterogeneous Information Network Embedding for Recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(2): 357-370.
[30]
IVANOV S, BURNAEV E. Anonymous Walk Embeddings [C]∥35th International Conference on Machine Learning. Stockholm: ACM, 2018:3448-3457.
[31]
DUTTA A, RIBA P, LIADOS J, Hierarchical Stochastic Graphlet Embedding for Graph-based Pattern Recognition[J]. Neural Computing & Applications, 2019, 32(15):11596-11597.

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        ICIIP '23: Proceedings of the 2023 8th International Conference on Intelligent Information Processing
        November 2023
        341 pages
        ISBN:9798400708091
        DOI:10.1145/3635175
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        Published: 21 December 2023

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        Author Tags

        1. entity-relationship extraction
        2. multi-attention mechanism
        3. remote supervision
        4. two-way selection

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