Abstract
Topic classification is useful for applications such as forensics analysis and cyber-crime investigation. To improve the overall performance on the task of Chinese conversation topic classification, we propose a hierarchical neural network with automatic semantic features selection, which is a hierarchical architecture that depicts the structure of conversations. The model firstly incorporates speaker information into the character- and word-level attentions and generates sentence representation, then uses attention-based BLSTM to construct the conversation representation. Experimental results on three datasets demonstrate that our model achieves better performance than multiple baselines. It indicates that the proposed architecture can capture the informative and salient features related to the meaning of a conversation for topic classification. And we release the dataset of this paper that can be obtained from https://github.com/njoe9/H-HANs.
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References
Orebaugh, A., Allnutt, J.: Classification of instant messaging communications for forensics analysis. Int. J. Forensic Comput. Sci. 1, 22–28 (2009)
Husin, N., Abdullah, M.T., Mahmod, R.: A systematic literature review for topic detection in chat conversation for cyber-crime investigation. Int. J. Digital Content Technol. Appl. 8(3), 22 (2014)
Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the 2015 Conference on EMNLP 2015, pp. 1422–1432 (2015)
Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: AAAI 2015, pp. 2267–2273 (2015)
Zhou, Y., Xu, B., Xu, J., Yang, L., Li, C., Xu, B.: Compositional recurrent neural networks for Chinese short text classification. In: 2016 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 137–144 (2016)
Kinsella, S., Passant, A., Breslin, J.G.: Topic classification in social media using metadata from hyperlinked objects. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 201–206. Springer, Heidelberg (2011). doi:10.1007/978-3-642-20161-5_20
Fei, G., Liu, B.: Social media text classification under negative covariate shift. In: EMNLP 2015, pp. 2347–2356 (2015)
Lee, K., Palsetia, D., Narayanan, R., Patwary, M.M.A., Agrawal, A., Choudhary, A.N.: Twitter trending topic classification. In: 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW), pp. 251–258 (2011)
Husby, S.D., Barbosa, D.: Topic classification of blog posts using distant supervision. In: Proceedings of the Workshop on Semantic Analysis in Social Media, pp. 28–36. Association for Computational Linguistics (2012)
Chen, Y., Li, Z., Nie, L., Hu, X., Wang, X., Chua, T., Zhang, X.: A semi-supervised Bayesian network model for microblog topic classification. In: COLING 2012, pp. 561–576 (2012)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on EMNLP, pp. 1746–1751 (2014)
Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: NIPS 2015, pp. 649–657 (2015)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS 2013, pp. 3111–3119 (2013)
Martins, A.F.T., Astudillo, R.F.: From softmax to sparsemax: a sparse model of attention and multi-label classification. In: ICML 2016, pp. 1614–1623 (2016)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: NAACL HLT 2016 (2016)
Zhou, Y., Xu, J., Cao, J., Xu, B., Li, C., Xu, B.: Hybrid attention networks for Chinese short text classification. In: CICLing 2017 (2017)
Bahdanau, D., Cho, K., Bengio, Y.: Neural Machine Translation by Jointly Learning to Align and Translate. CoRR (2014)
Zhang, Y., Er, M.J., Wang, N., Pratama, M.: Attention pooling-based convolutional neural network for sentence modelling. Inf. Sci. 373, 388–403 (2016)
Chen, H., Sun, M., Tu, C., Lin, Y., Liu, Z.: Neural sentiment classification with user and product attention. In: EMNLP 2016, pp. 1650–1659 (2016)
Acknowledgments
This work is supported by the National Natural Science Foundation (No. 61602479), National High Technology Research and Development Program of China (No. 2015AA015402) and National Key Technology R&D Program of China under No. 2015BAH53F02.
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Zhou, Y., Li, C., Xu, B., Xu, J., Cao, J., Xu, B. (2017). Hierarchical Hybrid Attention Networks for Chinese Conversation Topic Classification. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_56
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