Learning Topic-Oriented Word Embedding for Query Classification

  • Hebin Yang
  • Qinmin Hu
  • Liang He
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9077)


In this paper, we propose a topic-oriented word embedding approach to address the query classification problem. First, the topic information is encoded to generate query categories. Then, the user click-through information is also incorporated in the modified word embedding algorithms. After that, the short and ambiguous queries are enriched to be classified in a supervised learning way. The unique contributions are that we present four neural network strategies based on the proposed model. The experiments are designed on two open data sets, namely Baidu and Sogou, which are two famous commercial search companies. Our evaluation results show that the proposed approach is promising on both large data sets. Under the four proposed strategies, we achieve the high performance as 95.73% in terms of Precision, 97.79% in terms of the F1 measure.


Query classification Word embedding Word2vec Supervised learning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Beitzel, S.M., Jensen, E.C., Lewis, D.D., Chowdhury, A., Frieder, O.: Automatic classification of web queries using very large unlabeled query logs. ACM Transactions on Information Systems (TOIS) 25(2), 9 (2007)Google Scholar
  2. 2.
    Bengio, Y., Schwenk, H., Senécal, J.-S., Morin, F., Gauvain, J.-L.: Neural probabilistic language models. In: Holmes, D.E., Jain, L.C. (eds.) Neural ProbabilisticLanguage Models. StudFuzz, vol. 194, pp. 137–186. Springer, Heidelberg (2006)Google Scholar
  3. 3.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. the. Journal of Machine Learning Research 3, 993–1022 (2003)Google Scholar
  4. 4.
    Broder, A.Z., Fontoura, M., Gabrilovich, E., Joshi, A., Josifovski, V., Zhang, T.: Robust classification of rare queries using web knowledge. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 231–238. ACM (2007)Google Scholar
  5. 5.
    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)zbMATHGoogle Scholar
  6. 6.
    Ganti, V., König, A.C., Li, X.: Precomputing search features for fast and accurate query classification. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 61–70. ACM (2010)Google Scholar
  7. 7.
    Hinton, G.E.: Learning distributed representations of concepts. In: Proceedings of the Eighth Annual Conference of the Cognitive Science Society, vol. 1, p. 12. Amherst, MA (1986)Google Scholar
  8. 8.
    Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. arXiv preprint arXiv:1405.4053 (2014)
  9. 9.
    Li, X., Wang, Y.-Y., Shen, D., Acero, A.: Learning with click graph for query intent classification. ACM Transactions on Information Systems (TOIS) 28(3), 12 (2010)CrossRefGoogle Scholar
  10. 10.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
  11. 11.
    Mikolov, T., Karafiát, M., Burget, L., Cernockỳ, J., Khudanpur, S.: Recurrent neural network based language model. In: INTERSPEECH, pp. 1045–1048 (2010)Google Scholar
  12. 12.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011)zbMATHGoogle Scholar
  13. 13.
    Rei, L., Mladenic, D.: Learning semantic representations of words and their compositionality (2014)Google Scholar
  14. 14.
    Shen, D., Pan, R., Sun, J.-T., Pan, J.J., Wu, K., Yin, J., Yang, Q.: Query enrichment for web-query classification. ACM Transactions on Information Systems (TOIS), 24(3), 320–352 (2006)Google Scholar
  15. 15.
    Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Citeseer, pp. 1631–1642 (2013)Google Scholar
  16. 16.
    Sun, Y., Lin, L., Tang, D., Yang, N., Ji, Z., Wang, X.: Radical-enhanced chinese character embedding. arXiv preprint arXiv:1404.4714 (2014)
  17. 17.
    Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for twitter sentiment classification. ACL (2014)Google Scholar
  18. 18.
    Zelikovitz, S., Marquez, F.: Transductive learning for short-text classification problems using latent semantic indexing. International Journal of Pattern Recognition and Artificial Intelligence 19(02), 143–163 (2005)CrossRefGoogle Scholar
  19. 19.
    Zhang, M., Zhang, Y., Che, W., Liu, T.: Chinese parsing exploiting characters. ACL 1, 125–134 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyEast China Normal University ShanghaiShanghaiChina
  2. 2.Shanghai Key Laboratory of Multidimensional Information ProcessingEast China Normal UniversityShanghaiChina

Personalised recommendations