Abstract
In e-commerce websites, user-generated question-answering text pairs generally contain rich aspect information of products. In this paper, we address a new task, namely Question-answering (QA) aspect classification, which aims to automatically classify the aspect category of a given QA text pair. In particular, we build a high-quality annotated corpus with specifically designed annotation guidelines for QA aspect classification. On this basis, we propose a hierarchical attention network to address the specific challenges in this new task in three stages. Specifically, we firstly segment both question text and answer text into sentences, and then construct (sentence, sentence) units for each QA text pair. Second, we leverage a QA matching attention layer to encode these (sentence, sentence) units in order to capture the aspect matching information between the sentence inside question text and the sentence inside answer text. Finally, we leverage a self-matching attention layer to capture different importance degrees of different (sentence, sentence) units in each QA text pair. Experimental results demonstrate that our proposed hierarchical attention network outperforms some strong baselines for QA aspect classification.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
References
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12(Jul), 2121–2159 (2011)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th International Conference On Artificial Intelligence and Statistics, pp. 249–256 (2010)
He, R., Lee, W.S., Ng, H.T., Dahlmeier, D.: An unsupervised neural attention model for aspect extraction. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 388–397 (2017)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on EMNLP, pp. 1746–1751 (2014)
Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., McClosky, D.: The stanford corenlp natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of ACL: System Demonstrations, pp. 55–60. ACL (2014)
Mukherjee, A., Liu, B.: Aspect extraction through semi-supervised modeling. In: Proceedings of the 50th Annual Meeting of ACL, pp. 339–348. ACL (2012)
Poria, S., Cambria, E., Ku, L., Gui, C., Gelbukh, A.: A rule-based approach to aspect extraction from product reviews. In: Proceedings of the Second Workshop on Natural Language Processing for Social Media, pp. 28–37 (2014)
Rana, T.A., Cheah, Y.: A two-fold rule-based model for aspect extraction. Expert Syst. Appl. 89, 273–285 (2017)
Shu, L., Xu, H., Liu, B.: Lifelong learning CRF for supervised aspect extraction. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. 2, pp. 148–154 (2017)
Tang, D., Qin, B., Feng, X., Liu, T.: Effective LSTMs for target-dependent sentiment classification. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298–3307 (2016)
Toh, Z., Su, J.: NLANGP: supervised machine learning system for aspect category classification and opinion target extraction. In: International Workshop on Semantic Evaluation, pp. 496–501 (2015)
Wang, Y., Huang, M., Zhao, L., et al.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 606–615 (2016)
Xia, W., Zhu, W., Liao, B., Chen, M., Cai, L., Huang, L.: Novel architecture for long short-term memory used in question classification. Neurocomputing 299, 20–31 (2018)
Xue, W., Zhou, W., Li, T., Wang, Q.: MTNA: a neural multi-task model for aspect category classification and aspect term extraction on restaurant reviews. In: Proceedings of the 8th International Joint Conference on Natural Language Processing, pp. 151–156. Asian Federation of Natural Language Processing (2017)
Yang, Y., Liu, X.: A re-examination of text categorization methods. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 42–49 (1999)
Acknowledgements
This work is supported in part by Industrial Prospective Project of Jiangsu Technology Department under Grant No. BE2017081 and the National Natural Science Foundation of China under Grant No. 61572129.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, H., Liu, M., Wang, J., Xie, J., Shen, C. (2018). Question-Answering Aspect Classification with Hierarchical Attention Network. In: Sun, M., Liu, T., Wang, X., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2018 2018. Lecture Notes in Computer Science(), vol 11221. Springer, Cham. https://doi.org/10.1007/978-3-030-01716-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-01716-3_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01715-6
Online ISBN: 978-3-030-01716-3
eBook Packages: Computer ScienceComputer Science (R0)