基于迁移学习的文本分类算法研究
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摘要
迁移学习技术因其领域间知识、技能和经验的迁移能力,已成为跨领域文本分类的重要手段和研究热点。本文通过总结迁移学习在文本分类中的应用与发展情况,针对目前该领域存在的一些问题、难点进行了分析和研究,并提出几种新的迁移学习算法。其中,针对文本分类中普遍存在的维数灾难及特征词义不明确,而易导致分类精度过低及过拟合等问题,提出了集特征选取与抽取为一体的特征降维方法—HLK；针对文本分类中源领域与目标领域间文本数据在数量及相似性等方面的特点,分别提出两种基于实例的迁移学习方法-CGTL与IDRTAT;针对源领域数据集与目标领域数据集中数据分布差异过大,提出一种基于特征的迁移学习算法-BFRTL,并通过实验验证了各算法有效性。
Transfer-learning technique has become an important means of cross-cutting text classification and research focus, for its migration ability of knowledge, skills and experience. In this paper, by summarizing the application and development of transfer learning in text classification, the discussion and research are conducted for the problems and difficulties exist in the field, and several new transfer-learning algorithms are promoted. For the dimensionality disaster and undefined meaning of feature word exist in common text classification and easily lead to low classification accuracy and over-fitting problems, a feature dimension reduction algorithm HLK. based on feature selection and extraction was promoted. For the amount and similarity feature between texts of the original field and the target areas, two instance transfer learning methods CGTL and IDRTAT were proposed; for the significant difference in distribution between the data set of the original field and the target field, a feature representation transfer learning algorithm based on the feature was promoted, which was called BFRTL, and the feasibility of all algorithms were verified by experiments.
Transfer-learning technique has become an important means of cross-cutting text classification and research focus, for its migration ability of knowledge, skills and experience. In this paper, by summarizing the application and development of transfer learning in text classification, the discussion and research are conducted for the problems and difficulties exist in the field, and several new transfer-learning algorithms are promoted. For the dimensionality disaster and undefined meaning of feature word exist in common text classification and easily lead to low classification accuracy and over-fitting problems, a feature dimension reduction algorithm HLK. based on feature selection and extraction was promoted. For the amount and similarity feature between texts of the original field and the target areas, two instance transfer learning methods CGTL and IDRTAT were proposed; for the significant difference in distribution between the data set of the original field and the target field, a feature representation transfer learning algorithm based on the feature was promoted, which was called BFRTL, and the feasibility of all algorithms were verified by experiments.
引文
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4. M. E. Maron. Automatic indexing:An experimental inquiry. ACM 1961,8(3):404-417.
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7. Gerard Salton, Michael J. McGill. Introduction to Modern Information Retrieval,1986.
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14. R. Caruana. Multitask learning. Machine Learning,1997,28(1):41-75.
15.孟佳娜.迁移学习在文本分类中的应用研究[D].大连理工大学,2011：6-8
16. Xue X, Dai W, Yang Q, et al. Topic-bridged PLSA for cross-domain text classification. Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrival Singapore:ACM.2008.627-634.
17. Dai W, Jin O, Xue G, et al. Eigen Transfer:A unified framework for transfer learning. Proceedings of the 26th Annual International Conference on Machine Learning, Canada,2009:192-200.
18. Yi Yao, Gianfranco Doretto. Boosting for transfer learning with multiple sources. IEEE Conference on Computer Vision and Pattern Recocnition,2010:1855-1858.
19. Du W, Tan S, Chen X, et al. Adapting information bottleneck method for automatic construction of domain-oriented sentiment lexicon. Proceedings of the 3rd ACM Intenational Conference on Web Search and Data Mining,2010:111-120.
20. Yang T, Jin R, Jain K, et al. Unsupervised transfer classification:application to text categorization. Proceedings of the 16th ACM SIGKDD Conference on Knowledge and Data Mining Discovery, 2010:1159-1168.
21. Chen B, Lam W, Tsang I, et al.Extracting discriminative concepts for domain adaptation in text mining. Proceedings of the Fifteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2009:179-187.
22. Bickel S, Scheffer T. Dirichlet-enhanced spam filtering based on biased samples. Advances in Neural Information Proces-sing System,2007:161-168.
23. Zhang X, Dai W, Xue G, et al.Adaptive email spam filtering based on information theory. Proceedings of the 8th International Conference on Web Information Systems Engineering,2007:159-170.
24. Li T, Sindhwani V, Ding C, et al. Knowledge transformation from for cross-domain sentiment classification. Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2009:432-439.
25. Sinno Jialin Pan, Xiaochuan Ni, Jian-Tao Sun, et al. Cross-domain sentiment classification via spectral feature alignment. Proceedings of the19th international conference on World wide web,2010.
26.张紫琼,叶强,李一军.互联网商品评论情感分析研究综述[J].管理科学学报,2010,13(6)：87.
27. Blitzer J;Dredze M;Pereira F. Biographies, Bollywood,boom-boxes and blenders:Domain adaptation for sentiment classification. Proceedings of the45th Annual Meeting of the Association of Computational Linguistics,2007:432-439.
28. Salton G, Wong A, Yang C.S. A vector space model for auto matie indexing, Conununieations of the ACM,1975,18(11):613-620.
29. Dumais S.T, Platt J, Heckerman D, et al. Inductive learning algorithms and representations for text categorization, Proceedings of CIKM-98,7th ACM International Conference on Information and Knowledge Management,1998:148-155.
30. Nunzio G.M.D.A bidimensional view of doeuments for text eategorization, InProeeeding of the 26th European Conference on Information Retrieval Researeh,2004:112-126.
31. Bigi B.Using Kullback-Leibler distance for text categorization.In:Sebastiani F,ed.Proc.of the 25th European Conf.on Information Retrieval (ECIR-03).Pisa:Springer-Verlag,2003.305-319.
32. Caropreso M F, Matwin S, Sebastiani F.A learner-independent evaluation of the usefulness of statistical phrases for automated text categorization.Text Databases and Document Management: Theory and Practice,2001:78-102.
33.解冲锋,李星.基于序列的文本自动分类算法[J].软件学报,2002,13(4)：783-789.
34. Kehagias A, Petridis V, Kaburlasos V.G, et al.A Comparison of Word-and Sense-Based Text Categorization Using Several Classification Algorithms. Journal of Intelligent Information Systems,2003,21(3):227-247.
35. A. Moschitti and R. Basili. Complex linguistic features for text classication:a comprehensive study. Proceedings of ECIR-04,26th European Conference on Information Retrieval,2004:181-196.
36. Debole F, Sebastiani F. Supervised term weighting for automated text categorization. Proceedings of the 18th ACM Symp.on Applied Computing (SAC-03),2003:784-788.
37. Xue D, Sun M.Chinese text categorization based on the binary weighting model with non-binary smoothing.In:Sebastiani F, ed.Proc.of the 25th European Conf.on Information Retrieval (ECIR-03).Pisa:Springer-Verlag,2003:408-419.
38. Lertnattee V, Theeramunkong T. Effect of term distributions on centroid-based text categorization. Information Sciences,2004,158(1):89-115.
39. Me Callum A, et.al. A Comparison of Event Models for Naive Bayes Text Classification.AAAI-98 Workshop on Learning for Text Categoriza-tion,1998:509-516.
40. Belur V Dasarathy.Nearest Neighbor (NN) Norms:NN Pattern Classifica-tion Techniques,1991.
41. Burges J.C. A Tutorial on Support Vector Machines for Pattern Recognition. Bell Laboratories, Lucent Technologies.1997.
42. Rijsbergen. Information Retrieval. Butterworths:London,1979.
43. Ling C X, Huang J, Zhang H. AUC:A statistically consistent and more diseriminating measure than accuracy. In Proeeedings of 18th International Conference on Artifieial Intelligence,2003:329-341.
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