面向少量标注数据的命名实体识别研究(6)
[15] Ashiah V, Noam S, Niki P, et al. Attention Is All You Need[J]. arXiv preprint arXiv:, 2017.
[16] 李妮, 关焕梅, 杨飘,等. 基于BERT-IDCNN-CRF的中文命名实体识别方法[J]. 山东大学学报(理学版), 2020, 55(1):102-109.
[17] 王子牛, 姜猛, 高建瓴,等. 基于BERT的中文命名实体识别方法[J]. 计算机科学, 2019, 46(S2):138-142.
[18] Yosinski J, Clune J, Bengio Y, et al. How transferable are features in deep neural networks?[C]. Proceedings of the 28thConference on Neural Information Processing Systems(NIPS), 2014:3320-3328.
[19] Giorgi J M, Bader G D. Transfer learning for biomedical named entity recognition with neural networks[J]. Bioinformatics, 2018,34(23):4087-4094.
[20] Zhi L Y, Ruslan S, William W C. Transfer learning for sequence tagging with hierarchical recurrent networks[J]. arXiv preprint arXiv:, 2017.
[21] Sinno J P, Ivor W T, James T K, et al. Domain adaptation via transfer component analysis[J].IEEE Transactions on Neural Networks, 2010,22(2):199-210.
[22] Hal D. Frustratingly easy domain adaptation[C].Proceedings of the 45thAnnual Meeting of the Association of Computational :256-263.
[23] Young B K, Karl S, Rruhi S, et al. New transfer learning techniques for disparate label sets[C].Proceedings of the 53rdAnnual Meeting of the Association for Computational Linguistics and the 7thInternational Joint Conference on Natural Language Processing. 2015, 1:473-482.
[24] 孟创纪. 基于特征映射的深度迁移学习研究[D].兰州: 兰州大学, 2019.
[25] Lizhen Q, Gabriela F, Liyuan Z, et al. Named Entity Recognition for Novel Types by Transfer Learning[C]. Proceedings of the 2016 Conference on Empirical Methods in Natural Language :899-905.
[26] Bill Y L, Wei L. Neural Adaptation Layers for Crossdomain Named Entity Re-cognition[C]. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018:2012-2022.
[27] Jason A F, Sen W, Alexander R, et al. SwellShark:A Generative Model for Biomedical Named Entity Recognition without Labeled Data[J]. arXiv preprint arXiv:, 2017.
[28] Lee S, Song Y, Choi M, et al. Bagging-based active learning model for named entity recognition with distant supervision[C]. Proceedings of the 2016 International Conference on Big Data and Smart Computing (BigComp). IEEE, 2016:321-324.
[29] Alexander E R, Patrick S. Mining Wiki Resources for Multilingual Named Entity Recognition[C].Proceedings of the 46thAnnual Meeting of the Association for Computational Linguistics:Human Language Technologies. 2008:1-9.
[30] Xiao M P, Bo L Z, Jonathan M, et al. Cross-lingual name tagging and linking for 282 languages[C]. proceedings of the 55thAnnual Meeting of the Association for Computational Linguistics, 2017:1946-1958.
[31] Ren X, Wu Z, He W, et al. Cotype: Joint extraction of typed entities and relations with knowledge bases[C].proceedings of the 26thInternational Conference on World Wide Web. 2017:1015-1024.
[32] 史树敏, 冯冲, 黄河燕,等. 基于本体的汉语领域命名实体识别[J]. 情报学报, 2009, 28(6):857-863.
[33] Rinaldo L, Bernard E, Fred F. OntoILPER: an ontology- and inductive logic programmingbased system to extract entities and relations from text[J]. Knowledge and Information Systems, 2018,56(1):223-255.
[34] 李贯峰, 张鹏. 一个基于农业本体的Web知识抽取模型[J]. 江苏农业科学, 2018, 46(4):201-205.
[35] Chen Y K, Lasko T A, Mei Q Z, et al. A Study of Active Learning Methods for Named Entity Recognition in Clinical[J]. Journal of Biomedical Informatics, 2015(58):11-18.
[36] 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.
[37] Brooke J, Hammond A, Baldwin T. Bootstrapped text-level named entity recognition for literature[C].proceedings of the 54thAnnual Meeting of the Association for Computational Linguistics (Volume 2:Short Papers). 2016:344-350.
[38] Karadeniz I, ?zgür A. Linking entities through an ontology using word embeddings and syntactic reranking[J], BMC bioinformatics, 2019, 20(1):156.
[39] 程显毅, 谢璐, 朱建新,等. 生成对抗网络GAN综述[J]. 计算机科学, 2019, 46(3):74-81.
[40] Chen J, Xiao B L, Yue Z. Cross-Domain NER using Cross-Domain Language Modeling[C]. proceedings of the 57thAnnual Meeting of the Association for Computational Linguistics. 2019:2464-2474.
文章来源:《水产科技情报》 网址: http://www.sckjqbzz.cn/qikandaodu/2021/0303/398.html
上一篇:图书资料整理盒设计情报分析
下一篇:躬耕科研数十载勇立潮头传捷报广东省科技厅系