计算机科学 ›› 2020, Vol. 47 ›› Issue (2): 157-162.doi: 10.11896/jsjkx.190100167
钱小梅1,刘嘉勇1,程芃森1,2
QIAN Xiao-mei1,LIU Jia-yong1,CHENG Peng-sen1,2
摘要: 密集连接卷积神经网络(DenseNet)是一种新型深度卷积神经网络架构,通过建立不同层间的连接关系,来确保网络层与层间最大程度的信息传输。在文本远程监督关系抽取任务中,针对现有神经网络方法使用浅层网络提取特征的局限,设计了一种基于密集连接方式的深度卷积神经网络模型。该模型采用五层卷积神经网络构成的密集连接模块和最大池化层作为句子编码器,通过合并不同层次的词法、句法和语义特征,来帮助网络学习特征,从而获取输入语句更丰富的语义信息,同时减轻深度神经网络的梯度消失现象,使得网络对自然语言的表征能力更强。模型在NYT-Freebase数据集上的平均准确率达到了82.5%,PR曲线面积达到了0.43。实验结果表明,该模型能够有效利用特征,并提高远程监督关系抽取的准确率。
中图分类号:
[1]KUMAR S.A Survey of Deep Learning Methods for Relation Extraction[J].arXiv:1705.03645. [2]RINK B,HARABAGIU S.Utd:Classifying semantic relations by combining lexical and semantic resources[C]∥Proceedings of the 5th International Workshop on Semantic Evaluation.Association for Computational Linguistics,ACL Anthology,Stroudsburg,PA,2010:256-259. [3]BUNESCU R C,MOONEY R J.A shortest path dependency kernel for relation extraction[C]∥Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing.Association for Computational Linguistics(ACL),Stroudsburg,PA,2005:724-731. [4]ZENG D,LIU K,LAI S,et al.Relation classification via convolutional deep neural network[C]∥Proceedings of COLING 2014,the 25th International Conference on Computational Linguistics:Technical Papers.Association for Computational Linguistics,ACL Anthology,Stroudsburg,PA,2014:2335-2344. [5]EBRAHIMI J,DOU D.Chain based RNN for relation classification[C]∥Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.Association for Computational Linguistics (ACL),Stroudsburg,PA,2015:1244-1249. [6]XU Y,MOU L,LI G,et al.Classifying relations via long short term memory networks along shortest dependency paths[C]∥Proceedings of the 2015 conference on empirical methods in na-tural language processing.Association for Computational Linguistics (ACL),Stroudsburg,PA,2015:1785-1794. [7]MINTZ M,BILLS S,SNOW R,et al.Distant supervision for relation extraction without labeled data[C]∥Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP.Association for Computational Linguistics(ACL),Stroudsburg,PA,2009:1003-1011. [8]ZENG D,LIU K,CHEN Y,et al.Distant supervision for relation extraction via piecewise convolutional neural networks[C]∥Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.Association for Computational Linguistics (ACL),Stroudsburg,PA,2015:1753-1762. [9]LIN Y,SHEN S,LIU Z,et al.Neural relation extraction with selective attention over instances[C]∥Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.Association for Computational Linguistics (ACL),Stroudsburg,PA,2016:2124-2133. [10]ZHOU P,SHI W,TIAN J,et al.Attention-based bidirectional long short-term memory networks for relation classification[C]∥Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.Association for Computational Linguistics (ACL),Stroudsburg,PA,2016:207-212. [11]HUANG Y Y,WANG W Y.Deep Residual Learning for Weakly-Supervised Relation Extraction[J].arXiv:1707.08866. [12]HUANG G,LIU Z,VAN DER MAATEN L,et al.Densely connected convolutional networks[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscata-way,NJ,2017:4700-4708. [13]MIKOLOV T,SUTSKEVER I,CHEN K,et al.Distributed rep-resentations of words and phrases and their compositionality[C]∥Advances in Neural Information Processing Systems.Neural Information Processing Systems Foundation.2013:3111-3119. [14]NASRABADI N M.Pattern recognition and machine learning.[J].Journal of Electronic Imaging,2007,16(4):049901. [15]IOFFE S,SZEGEDY C.Batch normalization:Accelerating deep network training by reducing internal covariate shift[J].arXiv:1502.03167. [16]GLOROT X,BORDES A,BENGIO Y.Deep sparse rectifier neural networks[C]∥Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics.Microtome Publishing,Menlo Park,CA,2011:315-323. [17]SRIVASTAVA N,HINTON G,KRIZHEVSKY A,et al.Dropout:a simple way to prevent neural networks from overfitting[J].The Journal of Machine Learning Research,2014,15(1):1929-1958. [18]HE K,ZHANG X,REN S,et al.Deep residual learning for image recognition[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Socie-ty,Los Alamitos,CA,2016:770-778. [19]DE BOER P T,KROESE D P,MANNOR S,et al.A tutorial on the cross-entropy method[J].Annals of Operations Research,2005,134(1):19-67. [20]RIEDEL S,YAO L,MCCALLUM A.Modeling relations and their mentions without labeled text[C]∥Joint European Conference on Machine Learning and Knowledge Discovery in Databases.Berlin:Springer,2010:148-163. |
[1] | 周乐员, 张剑华, 袁甜甜, 陈胜勇. 多层注意力机制融合的序列到序列中国连续手语识别和翻译 Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion 计算机科学, 2022, 49(9): 155-161. https://doi.org/10.11896/jsjkx.210800026 |
[2] | 徐涌鑫, 赵俊峰, 王亚沙, 谢冰, 杨恺. 时序知识图谱表示学习 Temporal Knowledge Graph Representation Learning 计算机科学, 2022, 49(9): 162-171. https://doi.org/10.11896/jsjkx.220500204 |
[3] | 饶志双, 贾真, 张凡, 李天瑞. 基于Key-Value关联记忆网络的知识图谱问答方法 Key-Value Relational Memory Networks for Question Answering over Knowledge Graph 计算机科学, 2022, 49(9): 202-207. https://doi.org/10.11896/jsjkx.220300277 |
[4] | 汤凌韬, 王迪, 张鲁飞, 刘盛云. 基于安全多方计算和差分隐私的联邦学习方案 Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy 计算机科学, 2022, 49(9): 297-305. https://doi.org/10.11896/jsjkx.210800108 |
[5] | 陈泳全, 姜瑛. 基于卷积神经网络的APP用户行为分析方法 Analysis Method of APP User Behavior Based on Convolutional Neural Network 计算机科学, 2022, 49(8): 78-85. https://doi.org/10.11896/jsjkx.210700121 |
[6] | 朱承璋, 黄嘉儿, 肖亚龙, 王晗, 邹北骥. 基于注意力机制的医学影像深度哈希检索算法 Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism 计算机科学, 2022, 49(8): 113-119. https://doi.org/10.11896/jsjkx.210700153 |
[7] | 孙奇, 吉根林, 张杰. 基于非局部注意力生成对抗网络的视频异常事件检测方法 Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection 计算机科学, 2022, 49(8): 172-177. https://doi.org/10.11896/jsjkx.210600061 |
[8] | 檀莹莹, 王俊丽, 张超波. 基于图卷积神经网络的文本分类方法研究综述 Review of Text Classification Methods Based on Graph Convolutional Network 计算机科学, 2022, 49(8): 205-216. https://doi.org/10.11896/jsjkx.210800064 |
[9] | 李宗民, 张玉鹏, 刘玉杰, 李华. 基于可变形图卷积的点云表征学习 Deformable Graph Convolutional Networks Based Point Cloud Representation Learning 计算机科学, 2022, 49(8): 273-278. https://doi.org/10.11896/jsjkx.210900023 |
[10] | 王剑, 彭雨琦, 赵宇斐, 杨健. 基于深度学习的社交网络舆情信息抽取方法综述 Survey of Social Network Public Opinion Information Extraction Based on Deep Learning 计算机科学, 2022, 49(8): 279-293. https://doi.org/10.11896/jsjkx.220300099 |
[11] | 郝志荣, 陈龙, 黄嘉成. 面向文本分类的类别区分式通用对抗攻击方法 Class Discriminative Universal Adversarial Attack for Text Classification 计算机科学, 2022, 49(8): 323-329. https://doi.org/10.11896/jsjkx.220200077 |
[12] | 姜梦函, 李邵梅, 郑洪浩, 张建朋. 基于改进位置编码的谣言检测模型 Rumor Detection Model Based on Improved Position Embedding 计算机科学, 2022, 49(8): 330-335. https://doi.org/10.11896/jsjkx.210600046 |
[13] | 胡艳羽, 赵龙, 董祥军. 一种用于癌症分类的两阶段深度特征选择提取算法 Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification 计算机科学, 2022, 49(7): 73-78. https://doi.org/10.11896/jsjkx.210500092 |
[14] | 张颖涛, 张杰, 张睿, 张文强. 全局信息引导的真实图像风格迁移 Photorealistic Style Transfer Guided by Global Information 计算机科学, 2022, 49(7): 100-105. https://doi.org/10.11896/jsjkx.210600036 |
[15] | 戴朝霞, 李锦欣, 张向东, 徐旭, 梅林, 张亮. 基于DNGAN的磁共振图像超分辨率重建算法 Super-resolution Reconstruction of MRI Based on DNGAN 计算机科学, 2022, 49(7): 113-119. https://doi.org/10.11896/jsjkx.210600105 |
|