Leveraging External Knowledge to Enhance Query Model for Event Query

  • Wang PengmingEmail author
  • Li Peng
  • Li Rui
  • Wang Bin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10390)


Retrieval based on event query has recently become one of the most popular applications in information retrieval domain, whose goal is to retrieve event-related documents according to the given query about some specific event. However, using conventional retrieval method for this kind of task would usually be demonstrated with poor performance. To enhance query model and improve retrieval effectiveness for event query, an adaptive learning approach of PLSA model is presented in this paper. Through leveraging the knowledge of known coarse-grained events from external resource, the new approach can adaptively adjust the topic generative process of PLSA model on pseudo-relevance feedback documents, and learn the accurate language model for a particular topic, i.e., target event, which can be used to update the representation of users intention and finally improve the retrieval results. Experimental results on standard TREC collections show the proposed approach consistently outperform the state-of-the-art methods.


Language model Event-oriented retrieval Pseudo relevance feedback Topic model 



This work was supported by the National Natural Science Foundation of China (61572494, 61462027) and the fund project of Jiangxi Province Education Office (GJJ160529).


  1. 1.
    Allan, J., Papka, R., Lavrenko, V.: On-line new event detection and tracking. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 37–45. ACM (1998)Google Scholar
  2. 2.
    Becker, H., Iter, D., Naaman, M., Gravano, L.: Identifying content for planned events across social media sites. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 533–542. ACM (2012)Google Scholar
  3. 3.
    Glavaš, G., Šnajder, J.: Event-centered information retrieval using kernels on event graphs. In: TextGraphs-8 at Empirical Methods in Natural Language Processing (EMNLP 2013) (2013)Google Scholar
  4. 4.
    Yang, W., Li, R., Li, P., Zhou, M., Wang, B.: Event related document retrieval based on bipartite graph. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds.) WAIM 2016. LNCS, vol. 9658, pp. 467–478. Springer, Cham (2016). doi: 10.1007/978-3-319-39937-9_36 Google Scholar
  5. 5.
    Zhong, Z., Zhu, P., Li, C., Guan, Y., Liu, Z.: Research on event-oriented query expansion based on local analysis. J. China Soc. Sci. Tech. Inf. 31(2), 151–159 (2012)Google Scholar
  6. 6.
    Tsolmon, B., Lee, K.S.: An event extraction model based on timeline and user analysis in latent Dirichlet allocation. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1187–1190 (2014)Google Scholar
  7. 7.
    Quezada, M., Poblete, B.: Location-aware model for news events in social media. In: The International ACM SIGIR Conference, pp. 935–938 (2015)Google Scholar
  8. 8.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B (Methodological). 1–38 (1977)Google Scholar
  9. 9.
    Mclachlan, G.J., Krishnan, T.: The EM algorithm and extensions. Biometrics 382(1), 154–156 (2008)zbMATHGoogle Scholar
  10. 10.
    Zhai, C., Lafferty, J.: Model-based feedback in the language modeling approach to information retrieval. In: Proceedings of the Tenth International Conference on Information and Knowledge Management, pp. 403–410. ACM (2001)Google Scholar
  11. 11.
    Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to ad hoc information retrieval. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 334–342. ACM (2001)Google Scholar
  12. 12.
    Lv, Y., Zhai, C.: Negative query generation: bridging the gap between query likelihood retrieval models and relevance. Inf. Retr. J. 18(4), 359–378 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Information Engineering, Chinese Academy of SciencesBeijingChina
  2. 2.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina

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