Abstract
Restricted-domain question answering system gives high quality answer to questions within the domain, but gives no response or wrong answer for out of the domain questions. For normal users, the boundary of in-domain and out-domain is unclear. Most users often send out-domain inputs to the restricted-domain question answering system. In such cases, both no answer and wrong answer from the system will yield bad user experience. In this paper, an approach is proposed to solve the bad system response issue of the restricted-domain question answering system. Firstly, it uses a binary classifier to recognize in-domain user inputs and uses the restricted-domain question answering system to proved correct answer. Secondly, an user input taxonomy for out-domain user input is designed, and a classifier is trained to classify the out-domain user input based on the taxonomy. Finally, different response strategies are designed to response to different classes of out-domain user inputs. Experiments and actual application on a restricted-domain question answering system shows that the proposed approach is effective to improve user experience.
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.
References
Hu, H., Ren, F., Kuroiwa, S., Zhang, S.: A question answering system on special domain and the implementation of speech interface. In: Gelbukh, A. (ed.) CICLing 2006. LNCS, vol. 3878, pp. 458–469. Springer, Heidelberg (2006)
Mollá, D., Vicedo, J.L.: Question answering in restricted domains: An overview. Computational Linguistics 33(1), 41–61 (2007)
Lu, S., Chiang, D., Keh, H., et al.: Chinese text classification by the Naïve Bayes Classifier and the associative classifier with multiple confidence threshold values. Knowledge-based Systems 23, 598–604 (2010)
Zhang, D., Lee, W.S.: Question classification using support vector machines. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Toronto, Canada, July 28-August 1, pp. 26–32 (2003)
Pilászy, I.: Text categorization and support vector machine. In: The Proceedings of the 6th International Symposium of Hungarian Researchers on Computational Intelligence (2005)
He, X., Zhu, C., Zhao, T.: Research on short text classification for web forum. In: 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Shanghai, China, July 26-28, pp. 1052–1056 (2011)
Li, X., Roth, D.: Learning question classifiers: the role of semantic information. Natural Language Engineering 12(3), 229–249 (2006)
Bu, F., Zhu, X., Hao, Y., et al.: Function-based question classification for general QA. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Cambridge, Massachusetts, USA, October 09-11, pp. 1119–1128 (2010)
Fan, X., Hu, H.: A new model for chinese short-text classification considering feature extension. In: 2010 International Conference on Artificial Intelligence and Computational Intelligence (AICI), Sanya, China, October 23-24, pp. 7–11 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hou, Y., Wang, X., Chen, Q., Li, M., Tan, C. (2014). User Input Classification for Chinese Question Answering System. In: Wang, X., Pedrycz, W., Chan, P., He, Q. (eds) Machine Learning and Cybernetics. ICMLC 2014. Communications in Computer and Information Science, vol 481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45652-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-662-45652-1_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45651-4
Online ISBN: 978-3-662-45652-1
eBook Packages: Computer ScienceComputer Science (R0)