[1]王宇晖,杜军平,邵蓥侠.基于Transformer与技术词信息的知识产权实体识别方法[J].智能系统学报,2023,18(1):186-193.[doi:10.11992/tis.202203036]
 WANG Yuhui,DU Junping,SHAO Yingxia.An intellectual property entity recognition method based on Transformer and technological word information[J].CAAI Transactions on Intelligent Systems,2023,18(1):186-193.[doi:10.11992/tis.202203036]
点击复制

基于Transformer与技术词信息的知识产权实体识别方法

参考文献/References:
[1] 杨佳鑫, 杜军平, 邵蓥侠, 等. 面向知识产权的科技资源画像构建方法[J]. 软件学报, 2022, 33(4): 1439–1450
YANG Jiaxin, DU Junping, SHAO Yingxia, et al. Construction method of intellectual-property-oriented scientific and technological resources portrait[J]. Journal of software, 2022, 33(4): 1439–1450
[2] WANG Yuhui, DU Junping, SHAO Yingxia, et al. A patent text classification method based on phrase-context fusion feature[C]//Proceedings of 2021 Chinese Intelligent Automation Conference. Singapore: Springer, 2022: 157-164.
[3] XU Mingying, DU Junping, XUE Zhe, et al. A scientific research topic trend prediction model based on multi-LSTM and graph convolutional network[J]. International journal of intelligent systems, 2022, 37(9): 6331–6353.
[4] KOWSARI K, JAFARI M K, MOJTABA H, et al. Text classification algorithms: A survey[J]. Information, 2019, 10(4): 150.
[5] DEVLIN J, CHANG MING-WEI, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[EB/OL]. (2018?10?11)[2022?05?23].https://arxiv.org/abs/1810.04805.
[6] KOU Feifei, DU Junping, HE Yijiang, et al. Social network search based on semantic analysis and learning[J]. CAAI transactions on intelligence technology, 2016, 1(4): 293–302.
[7] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017: 30.
[8] CHEN Hui, LIN Zijia, DING Guiguang, et al. GRN: gated relation network to enhance convolutional neural network for named entity recognition[J]. Proceedings of the AAAI conference on artificial intelligence, 2019, 33(1): 6236–6243.
[9] SUN Bo, DU Junping, GAO Tian. Study on the improvement of K-nearest-neighbor algorithm[C]//2009 International Conference on Artificial Intelligence and Computational Intelligence. Shanghai: IEEE, 2009: 390-393.
[10] CHEN Tianci, LUO Mengfei, FU Hao, et al. Application of NER and association rules to traditional Chinese medicine patent mining[C]//2020 International Conferences on Internet of Things and IEEE Green Computing and Communications and IEEE Cyber, Physical and Social Computing and IEEE Smart Data and IEEE Congress on Cybermatics. Rhodes: IEEE, 2020: 767?772.
[11] XUE Zhe, DU Junping, DU Dawei, et al. Deep low-rank subspace ensemble for multi-view clustering[J]. Information sciences, 2019, 482: 210–227.
[12] FANG Yuke, DENG Weihong, DU Junping, et al. Identity-aware CycleGAN for face photo-sketch synthesis and recognition[J]. Pattern recognition, 2020, 102: 107249.
[13] KRESTEL R, CHIKKAMATH R, HEWEL C, et al. A survey on deep learning for patent analysis[J]. World patent information, 2021, 65: 102035.
[14] WANG Yu, LI Yun, ZHU Ziye, et al. SC-NER: a sequence-to-sequence model with sentence classification for named entity recognition[M]//Advances in Knowledge Discovery and Data Mining. Cham: Springer International Publishing, 2019: 198?209.
[15] SAAD F, ARAS H, HACKL-SOMMER R. Improving named entity recognition for biomedical and patent data using Bi-LSTM deep neural network models[M]//Natural Language Processing and Information Systems. Cham: Springer International Publishing, 2020: 25?36.
[16] ZHAI Zenan, NGUYEN D Q, AKHONDI S A, et al. Improving chemical named entity recognition in patents with contextualized word embeddings[EB/OL]. (2019?07?05) [2022?05?23].https://arxiv.org/abs/1907.02679.
[17] ZHANG Yue, YANG Jie. Chinese NER using lattice LSTM[EB/OL]. (2018?05?05)[2022?05?23].https://arxiv.org/abs/1805.02023.
[18] YAN Xingyu, XIONG Xiaofan, CHENG Xiufeng, et al. HMM-BiMM: hidden Markov model-based word segmentation via improved bi-directional maximal matching algorithm[J]. Computers & electrical engineering, 2021, 94: 107354.
[19] ZHAO Hongke, LIU Qi, ZHU Hengshu, et al. A sequential approach to market state modeling and analysis in online P2P lending[J]. IEEE transactions on systems, man, and cybernetics:systems, 2018, 48(1): 21–33.
[20] ALZAIDY R, CARAGEA C, GILES C L. Bi-LSTM-CRF sequence labeling for keyphrase extraction from scholarly documents[C]//WWW’19: The World Wide Web Conference. New York: ACM, 2019: 2551?2557.
[21] JIN Yanliang, XIE Jinfei, GUO Weisi, et al. LSTM-CRF neural network with gated self attention for Chinese NER[J]. IEEE access, 2019, 7: 136694–136703.
[22] LI Xiaonan, YAN Hang, QIU Xipeng, et al. FLAT: Chinese NER using flat-lattice transformer[EB/OL]. (2020-04-24)[2022-05-23].https://arxiv.org/abs/2004.11795.
[23] DAI Zihang, YANG Zhilin, YANG Yiming, et al. Transforme-xl: Attentive language models beyond a fixed-length context[EB/OL]. (2019?01?09)[2022?05?23].https://arxiv.org/abs/1901.02860.
[24] YAN Hang, DENG Bocao, LI Xiaonan, et al. TENER: adapting transformer encoder for named entity recognition[EB/OL]. (2019-11-10)[2022-05-23].https://arxiv.org/abs/1911.04474.
[25] YIN Xunwei, ZHENG Shuang, WANG Quanmin. Fine-grained Chinese named entity recognition based on RoBERTa-WWM-BiLSTM-CRF model[C]//2021 6th International Conference on Image, Vision and Computing. Qingdao: IEEE, 2021: 408?413.

备注/Memo

收稿日期:2022-03-21。
基金项目:国家重点研发计划项目(2018YFB1402600);国家自然科学基金项目(61772083).
作者简介:王宇晖,硕士研究生,CCF会员,主要研究方向为自然语言处理和数据挖掘;杜军平,教授,CCF会士,主要研究方向为人工智能、机器学习和模式识别。荣获吴文俊人工智能自然科学奖二等奖;邵蓥侠,副教授,CCF高级会员,主要研究方向为大规模图分析、并行计算框架和知识图谱分析
通讯作者:杜军平.E-mail:junpingdu@126.com

更新日期/Last Update: 1900-01-01
Copyright © 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134 邮箱:tis@vip.sina.com