Computer Science ›› 2016, Vol. 43 ›› Issue (12): 115-119.doi: 10.11896/j.issn.1002-137X.2016.12.020

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Research on Problem Classification Method Based on Deep Learning

LI Chao, CHAI Yu-mei, NAN Xiao-fei and GAO Ming-lei   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Question classification is an important part of question answering system.But question classification requires the strategy of extracting features and the continuous optimization of characteristic rules at the present stage.The methodof deep learning is feasible in the question classification by the way of self learning question characteristics to represent and understand the problem so as to avoid formulating artificial features and reduce labor costs.For question classification,the long-short term memory(LSTM) model and the convolution neural network (CNN) model were improved,combining the advantages of these two models into a new learning framework (LSTM-MFCNN) to strengthen the semantic study of word order and study of depth characteristics.Experimental results show that the proposed method still has good performance under the condition of no need to formulate the characteristic rules,and the accuracy of this me-thod is 93.08%.

Key words: Question classification,Deep learning,CNN,LSTM,Machine learning

[1] Hong L,Davison B D.A classification-based approach to question answering in discussion boards[C]∥Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2009:171-178
[2] Wang Y Z,Jia Y T,Liu D W,et al.Open Web Knowledge Aided Information Search and Data Mining[J].Computer Research and Development,2015,52(2):456-474(in Chinese) 王元卓,贾岩涛,刘大伟,等.基于开放网络知识的信息检索与数据挖掘[J].计算机研究与发展,2015,52(2):456-474
[3] Mudgal R,Madaan R,Sharma A K,et al.A Novel architecture for question classification based indexing scheme for efficient question answering[J].International Journal of Computer Engineering,2013,2(2):27-43
[4] Li X,Roth D.Learning question classifiers:the role of semantic information[J].Natural Language Engineering,2006,12(3):229-249
[5] Mishra M,Mishra V K,Sharma H R.Question Classification using Semantic,Syntactic and Lexical features[J].International Journal of Web & Semantic Technology,2013,4(3):39-47
[6] Zhang Y,Liu T,Wen X.Modified Bayesian Model Based Question Classification[J].Journal of Chinese Information Proces-sing,2005,9(2):100-105(in Chinese) 张宇,刘挺,文勖.基于改进贝叶斯模型的问题分类[J].中文信息学报,2005,19(2):100-105
[7] Wen X,Zhang Y,Liu T,et al.Syntactic Structure Parsing Based Chinese Question Classification[J].Journal of Chinese Information Processing,2006,20(2):33-39(in Chinese) 文勖,张宇,刘挺,等.基于句法结构分析的中文问题分类[J].中文信息学报,2006,20(2):33-39
[8] Sun J G,Cai D F,LV D X,et al.HowNet based Chinese question automatic classification[J].Journal of Chinese Information Processing,2007,21(1):90-95(in Chinese) 孙景广,蔡东风,吕德新,等.基于知网的中文问题自动分类[J].中文信息学报,2007,21(1):90-95
[9] Silva J,Coheur L,Mendes A C,et al.From symbolic to sub-symbolic information in question classification[J].Artificial Intelligence Review,2011,35(2):137-154
[10] Loni B,Van Tulder G,Wiggers P,et al.Question classification by weighted combination of lexical,syntactic and semantic features[C]∥Text,Speech and Dialogue.Springer Berlin Heidelberg,2011:243-250
[11] Liu L,Yu Z,Guo J,et al.Chinese question classification based on question property kernel[J].International Journal of Machine Learning and Cybernetics,2014,5(5):713-720
[12] Yadav R,Mishra M,Bhilai S.Question Classification UsingNave Bayes Machine Learning Approach[J].International Journal of Engineering and Innovative Technology (IJEIT),2013,2(8):291-294
[13] Socher R,Pennington J,Huang E H,et al.Semi-supervised recursive auto encoders for predicting sentiment distributions[C]∥Proceedings of the Conference on Empirical Methods in Natural Language Processing.Association for Computational Linguistics,2011:151-161
[14] Cui L,Zhang D,Liu S,et al.Learning topic representation for smt with neural networks[C]∥Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics.2014:133-143
[15] Blunsom P,Grefenstette E,Nal K,et al.A convolutional neural network for modelling sentences[C]∥Proceeding for the 52nd Annual Meeting of the Association for Computational Linguistics.2014:655-665
[16] Dong L,Wei F,Zhou M,et al.Question answering over freebase with multi-column convolutional neural networks[C]∥Procee-dings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing.2015:260-269
[17] Zhang D,Wang D.Relation Classification via Recurrent Neural Network[J].arXiv preprint arXiv:1508.01006,2015
[18] Kim Y.Convolutional neural networks for sentence classification[C]∥Empirical Methods in Natural Language Processing.2014:1746-1751
[19] Graves A.Generating sequences with recurrent neural networks[J].arXiv preprint arXiv:1308.0850,2013
[20] Hinton G E,Srivastava N,Krizhevsky A,et al.Improving neural networks by preventing coadaptation of feature detectors[J].arXiv preprint arXiv:1207.0580,2012
[21] Hinton G E.Learning distributed representations of concepts[C]∥Proceedings of the eighth annual conference of the cognitive science society.1986
[22] Yu Zheng-tao,Fan Xiao-zhong,Guo Jian-yi.Chinese QuestionClassification Based on Support Vector Machine[J].Journal of South China University of Technology,2005,33(9):25-29(in Chinese) 余正涛,樊孝忠,郭剑毅.基于支持向量机的汉语问句分类[J].华南理工大学学报,2005,33(9):25-29
[23] Tian W D,Gao Y Y ,Zu Y L.Question classification based on self-learning rules and modified Bayes[J].Application Research of Computers,2010,27(8):2869-2871(in Chinese) 田卫东,高艳影,祖永亮.基于自学习规则和改进贝叶斯结合的问题分类[J].计算机应用研究,2010,27(8):2869-2871
[24] Li R,Song X X,Wang W J.Chinese question classification based on Chinese FrameNet[J].Computer Engineering and Applications 2009,45(31):111-114(in Chinese) 李茹,宋小香,王文晶.基于汉语框架网的中文问题分类[J].计算机工程与应用,2009,45(31):111-114

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