[1] AGGARWAL C C, ZHAIC X. A survey of text classification algorithms[M]//Mining Text Data. Berlin:Springer, 2012:163-222. [2] KIM Y. Convolutional neural networks for sentence classification[C]//EMNLP 2014:Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA:Association for Computational Linguistics, 2014:1746-1751. [3] MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality[C]//NIPS'13:Proceedings of the 26th International Conference on Neural Information Processing Systems. West Chester, OH:Curran Associates Inc., 2013:3111-3119. [4] RAVUNI S, STOLCKE A. Recurrent neural network and LSTM models for lexical utterance classification[C]//ISCA2015:Proceedings of the 42nd International Symposium on Computer Architecture. Piscataway, NJ:IEEE, 2015:135-139. [5] LAI S W, XU L H, LIU K, et al. Recurrent convolutional neural networks for text classification[C]//Proceedings of the 2015 Twenty-Ninth AAAI Conference on Artificial Intelligence. Menlo Park, CA:AAAI, 2015:2267-2273. [6] ZHOU C T, SUN C L, LIU Z Y, et al. A C-LSTM neural network for text classification[EB/OL].[2017-11-12]. https://arxiv.org/pdf/1511.08630.pdf. [7] JOULIN A, GRAVE E, BOJANOWSKI P, et al. Bag of tricks for efficient text classification[C]//Proceedings of the 2017 Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg, PA:Association for Computational Linguistics, 2017:427-431. [8] 孟欣,左万利.基于word embedding的短文本特征扩展与分类[J].小型微型计算机系统,2017,38(8):1712-1717.(MENG X, ZUO W L. Short text expansion and classification based on word embedding[J]. Journal of Chinese Computer Systems, 2017, 38(8):1712-1717.) [9] 张猛.基于LDA的短文本分类中特征扩展方法的研究[D].北京:中国地质大学,2017.(ZHANG M. Feature extension method for short-text classification based on LDA[D]. Beijing:China University of Geosciences, 2017.) [10] 余本功,张连彬.基于CP-CNN的中文短文本分类研究[J].计算机应用研究,2018,35(4):1001-1004.(YU B G, ZHANG L B. Chinese short text classification based on CP-CNN[J]. Application Research of Computers, 2018, 35(4):1001-1004.) [11] 卢玲,杨武,杨有俊,等.结合语义扩展和卷积神经网络的中文短文本分类方法[J].计算机应用,2017,37(12):3498-3503.(LU L, YANG W, YANG Y J, et al. Chinese short text calssification method by combining semantic expansion and convolutional neural network[J]. Journal of Computer Applications, 2017, 37(12):3498-3503.) [12] MADJAROV G, KOCEV D, GJORGJVIKJ D, et al. An extensive experimental comparison of methods for multi-label learning[J]. Pattern Recognition, 2012, 45(9):3084-3104. [13] BOUTELL M R, LUO J, SHEN X, et al. Learning multi-label scene classification[J]. Pattern Recognition, 2004, 37(9):1757-1771. [14] BRINKER K. Multilabel classification via calibrated label ranking[J]. Machine Learning, 2008, 73(2):133-153. [15] TSOUMAKAS G, VLANHAVAS I. Random k-labelsets:an ensemble method for multilabel classification[C]//ECML 2007:Proceedings of the 18th European Conference on Machine Learning. Berlin:Springer, 2007:406-417. [16] ZHANG M L, ZHOU Z H. ML-KNN:a lazy learning approach to multi-label learning[J]. Pattern Recognition, 2007, 40(7):2038-2048. [17] ELISSEEFF A, WESTON J. A kernel method for multi-labelled classification[C]//NIPS'01:Proceedings of the 14th International Conference on Neural Information Processing Systems:Natural and Synthetic. Cambridge, MA:MIT Press, 2001:681-687. [18] ZHANG M L, ZHANG K. Multi-label learning by exploiting label dependency[C]//Proceedings of the 2010 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2010:999-1008. [19] WU F, WANG Z, ZHANG Z, et al. Weakly semi-supervised deep learning for multi-label image annotation[J]. IEEE Transactions on Big Data, 2017, 1(3):109-122. [20] CHEN G, YE D, XING Z, et al. Ensemble application of convolutional and recurrent neural networks for multi-label text categorization[C]//Proceedings of the 2017 International Joint Conference on Neural Networks. Piscataway, NJ:IEEE, 2017:2377-2383. [21] 韩栋,王春华,肖敏.结合旋转森林和AdaBoost分类器的多标签文本分类方法[J/OL].计算机应用研究,2018,35(12)[2017-12-08]. http://www.arocmag.com/article/02-2018-12-014.html.(HAN D, WANG C H, XIAO M. Multi-label text classification method based on rotating forest and AdaBoost classifier[J/OL]. Application Research of Computers, 2018, 35(12)[2017-12-08]. http://www.arocmag.com/article/02-2018-12-014.html.) [22] 张晶,李裕,李培培.基于随机子空间的多标签类属特征提取算法[J/OL].计算机应用研究,2019,36(2) (2017-05-12)[2018-01-19]. http://www.arocmag.com/article/02-2019-02-012.html.(ZHANG J, LI Y, LI P P. Multi-label label-specific feature extraction algorithm based on random subspace[J/OL]. Application Research of Computers, 2019, 36(2) (2017-05-12)[2018-01-19]. http://www.arocmag.com/article/02-2019-02-012.html.) [23] 孙松涛,何炎祥.基于CNN特征空间的微博多标签情感分类[J].工程科学与技术,2017,49(3):162-169.(SUN S T, HE Y X. Multi-label emotion classification for microblog based on CNN feature space[J]. Advanced Engineering Science, 2017, 49(3):162-169.) [24] SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout:a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1):1929-1958. [25] VORONSOV M A, SIVOKON V. Stochastic parallel-gradient-descent technique for high-resolution wave-front phase-distortion correction[J]. Journal of the Optical Society of America A, 1998, 15(10):2745-2758. |