参考文献/References:
[1] 李涛,王次臣,李华康. 知识图谱的发展与构建[J]. 南京理工大学学报,2017,41(1):22-34.
Li Tao,Wang Cichen,Li Huakang. Development and construction of knowledge graph[J]. Journal of Nanjing University of Science and Technology,2017,41(1):22-34.
[2]王雍凯,毛存礼,余正涛,等. 基于图的新闻事件主题句抽取方法[J]. 南京理工大学学报,2016,40(4):438-444.
Wang Yongkai,Mao Cunli,Yu Zhengtao,et al. Approach for topical sentence of news events extraction based on graph[J]. Journal of Nanjing University of Science and Technology,2016,40(4):438-444.
[3]杨玉娟,袁欢欢,王永利. 针对评论文本的情感分析方法[J]. 南京理工大学学报,2019,43(3):280-285.
Yang Yujuan,Yuan Huanhuan,Wang Yongli. Sentiment analysis method for comment text[J]. Journal of Nanjing University of Science and Technology,2019,43(3):280-285.
[4]Zhou Guodong,Su Jian. Named entity recognition using an HMM-based chunk tagger[C]//Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. Philadelphia,Pennsylvania,USA:Association for Computational Linguistics,2002:473-480.
[5]Chieu H L,Ng H T. Named entity recognition:A maximum entropy approach using global information[C]//Proceedings of the 19th international conference on Computational linguistics. Philadelphia,Pennsylvania,USA:Association for Computational Linguistics,2002:1-7.
[6]McCallum A,Freitag D,Pereira F C N. Maximum entropy Markov models for information extraction and segmentation[C]//Proceedings of the Seventeenth International Conference on Machine Learning. San Francisco,California,USA:Morgan Kaufmann Publishers Inc,2000:591-598.
[7]Lafferty J D,McCallum A,Pereira F C N. Conditional random fields:Probabilistic models for segmenting and labeling sequence data[C]//Proceedings of the Eighteenth International Conference on Machine Learning. San Francisco,California,USA:Morgan Kaufmann Publishers Inc,2001:282-289.
[8]Chiu J P C,Nichols E. Named entity recognition with bidirectional LSTM-CNNs[J]. Transactions of the Association for Computational Linguistics,2016(4):357-370.
[9]Rrubaa Panchendrarajan,Aravindh Amaresan. Bidirectional LSTM-CRF for Named Entity Recognition[C]. //32nd Pacific Asia Conference on Language,Information and Computation. Hong Kong,China:Association for Computational Linguistics,2018:531-540.
[10]Lample G,Ballesteros M,Subramanian S,et al. Neural architectures for named entity recognition[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. San Diego,California,USA:Association for Computational Linguistics,2016:260-270.
[11]Vikas Yadav,Rebecca Sharp,Steven Bethard. Deep affix features improve neural named entity recognizers[C]//Proceedings of the 7th Joint Conference on Lexical and Computational Semantics. New Orleans,Louisiana,USA:Association for Computational Linguistics,2018:167-172.
[12]Rohini Srihari,Cheng Niu,Wei Li. A hybrid approach for named entity and sub-type tagging[C]//Proceedings of the 6th Conference on Applied Natural Language Processing. Seattle,Washington,USA:Association for Computational Linguistics,2000,247-254.
[13]李明扬,孔芳. 融入自注意力机制的社交媒体命名实体识别[J]. 清华大学学报(自然科学版),2019,59(6):461-467.
Li Mingyang,Kong Fang. Combined self-attention mechanism for named entity recognition in social media[J]. Journal of Tsinghua University(Science and Technology),2019,59(6):461-467.
[14]殷章志,李欣子,黄德根,李玖一. 融合字词模型的中文命名实体识别研究[J]. 中文信息学报,2019,33(11):95-100.
Yin Zhangzhi,Li Xinzi,Huang Degen,et al. Chinese named entity recognition ensembled with character[J]. Journal of Chinese Information Processing,2019,33(11):95-100.
[15]林广和,张绍武,林鸿飞. 基于细粒度词表示的命名实体识别研究[J]. 中文信息学报,2018,32(11):62-71.
Lin Guanghe,Zhang Shaowu,Lin Hongfei. Named entity identification based on fine-grained word representation[J]. Journal of Chinese Information Processing,2018,32(11):62-71.
[16]Graves A,Schmidhuber J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures[J]. Neural Networks,2005,18(5-6):602-610.
[17]Graves A,Mohamed A R,Hinton G. Speech recognition with deep recurrent neural networks[C]//2013 IEEE International Conference on Acoustics,Speech and Signal Processing,Vancouver,Canada:IEEE,2013:6645-6649.
相似文献/References:
[1]张少辉,王迤冉.用于图像识别的稀疏高斯编码[J].南京理工大学学报(自然科学版),2016,40(01):61.
Zhang Shaohui,Wang Yiran.Sparse Gaussian coding for image recognition[J].Journal of Nanjing University of Science and Technology,2016,40(06):61.
[2]王 林,董 楠.基于Gabor特征与卷积神经网络的人体轮廓提取[J].南京理工大学学报(自然科学版),2018,42(01):89.[doi:10.14177/j.cnki.32-1397n.2018.42.01.013]
Wang Lin,Dong Nan.Human silhouette identification based on Gabor featureand convolutional neural network[J].Journal of Nanjing University of Science and Technology,2018,42(06):89.[doi:10.14177/j.cnki.32-1397n.2018.42.01.013]
[3]姚富光,钟先信,周靖超.粒计算:一种大数据融合智能建模新方法[J].南京理工大学学报(自然科学版),2018,42(04):503.[doi:10.14177/j.cnki.32-1397n.2018.42.04.017]
Yao Fuguang,Zhong Xianxin,Zhou Jingchao.Granular computing:a new method of intelligent modelingfor big data fusion[J].Journal of Nanjing University of Science and Technology,2018,42(06):503.[doi:10.14177/j.cnki.32-1397n.2018.42.04.017]
[4]吕 鲜,戚 湧,张伟斌.基于长短期记忆模型的交通拥堵预测方法[J].南京理工大学学报(自然科学版),2020,44(01):26.[doi:10.14177/j.cnki.32-1397n.2020.44.01.005]
Lv Xian,Qi Yong,Zhang Weibin.Traffic congestion prediction method based onlong short-term memory model[J].Journal of Nanjing University of Science and Technology,2020,44(06):26.[doi:10.14177/j.cnki.32-1397n.2020.44.01.005]
[5]印 杰,蒋宇翔,牛博威,等.基于深度学习的网页篡改远程检测研究[J].南京理工大学学报(自然科学版),2020,44(01):49.[doi:10.14177/j.cnki.32-1397n.2020.44.01.008]
Yin Jie,Jiang Yuxiang,Niu Bowei,et al.Remote detection of web page tampering based on deep learning[J].Journal of Nanjing University of Science and Technology,2020,44(06):49.[doi:10.14177/j.cnki.32-1397n.2020.44.01.008]
[6]张德磊,宋晓宁,於东军.基于统一划分的特征自适应行人再识别方法[J].南京理工大学学报(自然科学版),2020,44(03):266.[doi:10.14177/j.cnki.32-1397n.2020.44.03.002]
Zhang Delei,Song Xiaoning,Yu Dongjun.Feature adaptive person re-identification method
based on unified partition[J].Journal of Nanjing University of Science and Technology,2020,44(06):266.[doi:10.14177/j.cnki.32-1397n.2020.44.03.002]