Abstract
User intent identification and classification has become a vital topic of query understanding in human-computer dialogue applications. The identification of users’ intent is especially crucial for assisting system to understand users’ queries so as to classify the queries accurately to improve users’ satisfaction. Since the posted queries are usually short and lack of context, conventional methods heavily relying on query n-grams or other common features are not sufficient enough. This paper proposes a compact yet effective user intention classification method named as ST-UIC based on a constructed semantic tag repository. The method proposes to use a combination of four kinds of features including characters, non-key-noun part-of-speech tags, target words, and semantic tags. The experiments are based on a widely applied dataset provided by the First Evaluation of Chinese Human-Computer Dialogue Technology. The result shows that the method achieved a F1 score of 0.945, exceeding a list of baseline methods and demonstrating its effectiveness in user intent classification.
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References
Zhang, W.-N., Chen, Z., Che, W., Hu, G., Liu, T.: The First Evaluation of Chinese Human-Computer Dialogue Technology. arXiv preprint arXiv:1709.10217 (2017)
Zue, V., Seneff, S.: Spoken dialogue systems. Synth. Lect. Hum. Lang. Technol. 2, 1–151 (2009)
Tur, G., Deng, L., Hakkani-Tür, D., He, X.: Towards deeper understanding: deep convex networks for semantic utterance classification. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5045–5048 (2012)
Zhang, J., Yang, T.Z., Hazen, T.J.: Large-scale word representation features for improved spoken language understanding. In: International Conference on Acoustics, Speech and Signal Processing, pp. 5306–5310 (2015)
Liu, J., Pasupat, P., Wang, Y., Cyphers, S., Glass, J.: Query understanding enhanced by hierarchical parsing structures. In: IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 72–77 (2013)
Liu, B., Lane, I.: Multi-Domain Adversarial Learning for Slot Filling in Spoken Language Understanding. arXiv preprint arXiv:1711.11310. pp. 1–6 (2017)
Xu, P., Sarikaya, R.: Contextual domain classification in spoken language understanding systems using recurrent neural network. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3–7 (2014)
Ashkan, A., Clarke, C.L.A., Agichtein, E., Guo, Q.: Classifying and characterizing query intent. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 578–586. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00958-7_53
Hu, J., Wang, G., Lochovsky, F., Sun, J., Chen, Z.: Understanding user’s query intent with Wikipedia. In: International Conference on World Wide Web, pp. 471–480. ACM (2009)
Park, K., Jee, H., Lee, T., Jung, S., Lim, H.: Automatic extraction of user’s search intention from web search logs. Multimed. Tools Appl. 61, 145–162 (2012)
Jansen, B.J., Booth, D.L., Spink, A.: Determining the user intent of web search engine queries. In: International Conference on World Wide Web, pp. 1149–1150. ACM (2007)
Zhang, S., Wang, B.: A survey of web search query intention classification. J. Chin. Inf. Process. 22(4), 75–82 (2008)
Yu, H., Liu, Y., Zhang, M., Ru, L., Ma, S.: Research in search engine user behavior based on log analysis. J. Chin. Inf. Process. 21, 109–114 (2007)
De Mori, R., Béchet, F., Hakkani-Tür, D., McTear, M., Riccardi, G., Tur, G.: Spoken language understanding: a survey. In: Automatic Speech Recognition and Understanding Workshop, pp. 365–376 (2007)
Hao, T., Xie, W., Wu, Q., Weng, H., Qu, Y.: Leveraging question target word features through semantic relation expansion for answer type classification. Knowl. Based Syst. 133, 43–52 (2017)
Hao, T., Xie, W., Xu, F.: A wordnet expansion-based approach for question targets identification and classification. In: Sun, M., Liu, Z., Zhang, M., Liu, Y. (eds.) CCL 2015. LNCS (LNAI), vol. 9427, pp. 333–344. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25816-4_27
Gorin, A., Riccardi, G., Wright, J.: How may I help you? Speech Commun. 23, 113–127 (1997)
Gupta, N., Tur, G., Hakkani-Tur, D., Bangalore, S., Riccardi, G., Gilbert, M.: The AT&T spoken language understanding system. IEEE Trans. Audio Speech Lang. Process. 14(1), 213–222 (2006)
Hakkani-Tür, D., Heck, L., Tur, G.: Exploiting query click logs for utterance domain detection in spoken language understanding. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5636–5639 (2011)
Celikyilmaz, A., Hakkani-Tür, D., Tur, G.: Leveraging web query logs to learn user intent via bayesian discrete latent variable model. In: International Conference on Machine Learning (2011)
Hernández, I., Gupta, D., Rosso, P., Rocha, M.: A simple model for classifying web queries by user intent. In: Spanish Conference Information Retrieval, pp. 235–240 (2012)
Ganti, V., König, A.C., Li, X.: Precomputing search features for fast and accurate query classification. In: ACM International Conference on Web Search and Data Mining, pp. 61–70 (2010)
Deng, L., Tur, G., He, X., Hakkani-Tur, D.: Use of kernel deep convex networks and end-to-end learning for spoken language understanding. In: IEEE Workshop on Spoken Language Technology, pp. 210–215 (2012)
Shi, Y., Yao, K., Chen, H., Pan, Y.-C.Y., Hwang, M.-Y., Peng, B.: Contextual spoken language understanding using recurrent neural networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 5271–5275 (2015)
Bengio, Y.: Deep learning of representations: looking forward. In: Dediu, A.-H., Martín-Vide, C., Mitkov, R., Truthe, B. (eds.) SLSP 2013. LNCS (LNAI), vol. 7978, pp. 1–37. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39593-2_1
Acknowledgements
This work was supported by National Natural Science Foundation of China (No.61772146) and Innovative School Project in Higher Education of Guangdong Province (No.YQ2015062). Guangzhou Science Technology and Innovation Commission (No. 201803010063).
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Xie, W., Gao, D., Ding, R., Hao, T. (2018). A Feature-Enriched Method for User Intent Classification by Leveraging Semantic Tag Expansion. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_19
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