计算机科学 ›› 2018, Vol. 45 ›› Issue (6): 235-240.doi: 10.11896/j.issn.1002-137X.2018.06.042
周枫, 李荣雨
ZHOU Feng, LI Rong-yu
摘要： 针对深度学习在处理文本分类问题时存在的适应度小、精确度较低等问题,提出一种采用双向门控循环单元(BGRU)进行池化的改进卷积神经网络模型。在池化阶段,将BGRU产生的中间句子表示与由卷积层得到的局部表示进行对比,将相似度高的判定为重要信息,并通过增大其权重来保留此信息。该模型可以进行端到端的训练,对多种类型的文本进行训练,适应性较强。实验结果表明,相较于其他同类模型,提出的改进模型在学习能力上有较大优势,分类精度也有显著提高。
中图分类号:
| [1]SEBASTIANI F.Machine learning in automated text categorization[J].Acm Computing Surveys,2001,34(1):1-47. [2]LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436-444. [3]GUO L L,DING S F.Research Progress on Deep Learning[J].Computer Science,2015,42(5):28-33.(in Chinese) 郭丽丽,丁世飞.深度学习研究进展[J].计算机科学,2015,42(5):28-33. [4]KIM Y.Convolutional neural networks for sentence classification[J].arXiv preprint arXiv:1408.5882,2014. [5]KALCHBRENNER N,GREFENSTETTE E,BLUNSOM P.A convolutional neural network for modelling sentences[J].arXiv preprint arXiv:1404.2188,2014. [6]QIN P,XU W,GUO J.An empirical convolutional neural network approach for semantic relation classification[J].Neurocomputing,2016,190:1-9. [7]LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-based learning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324. [8]WANG P,XU B,XU J,et al.Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification[J].Neurocomputing,2016,174:806-814. [9]BOUREAU Y L,ROUX N L,BACH F,et al.Ask the locals:multi-way local pooling for image recognition[C]//2011 IEEE International Conference on Computer Vision (ICCV).IEEE,2011:2651-2658. [10]LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-based learning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324. [11]HOCHREITER S,SCHMIDHUBER J,et al.Long short-term memory[J].Neural Computer,1998,9(8):1735-1780. [12]CHUNG J,GULCEHRE C,CHO K H,et al.Empirical evaluation of gated recurrent neural networks on sequence modeling[J].arXiv preprint arXiv:1412.3555,2014. [13]YONG Z,MENG J E,NING W,et al.Attention Pooling-based Convolutional Neural Network for Sentence Modelling[J].Information Sciences,2016,373(C):388-403. [14]GREFF K,SRIVASTAVA R K,KOUTNÍK J,et al.LSTM:A search space odyssey[J].IEEE Transactions on Neural Networks and Learning Systems,2017,28(10):2222-2232. [15]ZHANG Y,WALLACE B.A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification[J].arXiv preprint arXiv:1510.03820,2015. [16]MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space[J].arXiv preprint arXiv:1301.3781,2013. [17]ZHANG C Y,QIN P D,YI Y L.Self-adaptation Multi-gram weight learning strategy for sentence representation based on convolutional neural network[J].Computer Science,2017,44(1):60-64.(in Chinese) 张春云,秦鹏达,尹义龙.基于卷积神经网络的自适应权重multi-gram语句建模系统[J].计算机科学,2017,44(1):60-64. |
| [1] | 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 |
| [2] | 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 |
| [3] | 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 |
| [4] | 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 |
| [5] | 武红鑫, 韩萌, 陈志强, 张喜龙, 李慕航. 监督和半监督学习下的多标签分类综述 Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning 计算机科学, 2022, 49(8): 12-25. https://doi.org/10.11896/jsjkx.210700111 |
| [6] | 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 |
| [7] | 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 |
| [8] | 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 |
| [9] | 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 |
| [10] | 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 |
| [11] | 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 |
| [12] | 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 |
| [13] | 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 |
| [14] | 闫佳丹, 贾彩燕. 基于双图神经网络信息融合的文本分类方法 Text Classification Method Based on Information Fusion of Dual-graph Neural Network 计算机科学, 2022, 49(8): 230-236. https://doi.org/10.11896/jsjkx.210600042 |
| [15] | 侯钰涛, 阿布都克力木·阿布力孜, 哈里旦木·阿布都克里木. 中文预训练模型研究进展 Advances in Chinese Pre-training Models 计算机科学, 2022, 49(7): 148-163. https://doi.org/10.11896/jsjkx.211200018 |
|
||
首页