Abstract
Distributed representations have gained a lot of interests in natural language processing community. In this paper, we propose a method to learn document embedding with neural network architecture for text classification task. In our architecture, each document can be represented as a fine-grained representation of different meanings so that the classification can be done more accurately. The results of our experiments show that our method achieve better performances on two popular datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Androutsopoulos, I., Koutsias, J., Chandrinos, K.V., Paliouras, G., Spyropoulos, C.D.: An evaluation of naive bayesian anti-spam filtering. arXiv preprint cs/0006013 (2000)
Bengio, Y., Schwenk, H., Senécal, J.S., Morin, F., Gauvain, J.L.: Neural probabilistic language models. In: Holmes, D.E., Jain, L.C. (eds.) Innovations in Machine Learning. STUDFUZZ, vol. 194, pp. 137–186. Springer, Heidelberg (2006)
Carvalho, V.R., Cohen, W.W.: On the collective classification of email speech acts. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 345–352. ACM (2005)
Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST)Â 2(3), 27 (2011)
Cohen, W.W.: Learning rules that classify e-mail. In: AAAI Spring Symposium on Machine Learning in Information Access, California, vol. 18, p. 25 (1996)
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. The Journal of Machine Learning Research 12, 2493–2537 (2011)
Dumais, S., Platt, J., Heckerman, D., Sahami, M.: Inductive learning algorithms and representations for text categorization. In: Proceedings of the Seventh International Conference on Information and Knowledge Management, pp. 148–155. ACM (1998)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Huang, E.H., Socher, R., Manning, C.D., Ng, A.Y.: Improving word representations via global context and multiple word prototypes. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-vol. 1, pp. 873–882. Association for Computational Linguistics (2012)
Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. Springer (1998)
Khosravi, H., Wilks, Y.: Routing email automatically by purpose not topic. Natural Language Engineering 5(3), 237–250 (1999)
Larochelle, H., Bengio, Y.: Classification using discriminative restricted boltzmann machines. In: Proceedings of the 25th International Conference on Machine Learning, pp. 536–543. ACM (2008)
Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. arXiv preprint arXiv:1405.4053 (2014)
Liu, T.: A novel text classification approach based on deep belief network. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) ICONIP 2010, Part I. LNCS, vol. 6443, pp. 314–321. Springer, Heidelberg (2010)
McCallum, A., Nigam, K., et al.: A comparison of event models for naive bayes text classification. In: AAAI 1998 Workshop on Learning for Text Categorization, vol. 752, pp. 41–48. Citeseer (1998)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Mikolov, T., Yih, W.T., Zweig, G.: Linguistic regularities in continuous space word representations. In: Proceedings of NAACL-HLT, pp. 746–751 (2013)
Mitchell, J., Lapata, M.: Composition in distributional models of semantics. Cognitive Science 34(8), 1388–1429 (2010)
Mnih, A., Hinton, G.E.: A scalable hierarchical distributed language model. In: NIPS, pp. 1081–1088 (2008)
Nasr, G.E., Badr, E., Joun, C.: Cross entropy error function in neural networks: Forecasting gasoline demand. In: FLAIRS Conference, pp. 381–384 (2002)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1(1), 81–106 (1986)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. MIT Press, Cambridge (1988)
Salton, G., Wong, A., Yang, C.: A vector space model for automatic indexing. Communications of the ACM 18(11), 613–620 (1975)
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)
Socher, R., Lin, C.C., Ng, A.Y., Manning, C.D.: Parsing Natural Scenes and Natural Language with Recursive Neural Networks. In: Proceedings of the 26th International Conference on Machine Learning, ICML (2011)
Yang, Y., Pedersen, J.: A comparative study on feature selection in text categorization. In: Proc. of Int. Conf. on Mach. Learn. (ICML), vol. 97 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Huang, C., Qiu, X., Huang, X. (2014). Text Classification with Document Embeddings. In: Sun, M., Liu, Y., Zhao, J. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2014 2014. Lecture Notes in Computer Science(), vol 8801. Springer, Cham. https://doi.org/10.1007/978-3-319-12277-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-12277-9_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12276-2
Online ISBN: 978-3-319-12277-9
eBook Packages: Computer ScienceComputer Science (R0)