Abstract
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.
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
Notes
- 1.
This is reasonable as, in general, we only need to confirm the term distribution in unknown event not be consistent with any known event language model.
- 2.
In this paper, we don’t have any prior knowledge in this step, so we can only use the same initial value for all \(\lambda \).
- 3.
See http://www.trec-ts.org/ for details.
- 4.
Available at http://lucene.apache.org/.
References
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)
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)
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)
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
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)
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)
Quezada, M., Poblete, B.: Location-aware model for news events in social media. In: The International ACM SIGIR Conference, pp. 935–938 (2015)
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)
Mclachlan, G.J., Krishnan, T.: The EM algorithm and extensions. Biometrics 382(1), 154–156 (2008)
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)
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)
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)
Acknowledgment
This work was supported by the National Natural Science Foundation of China (61572494, 61462027) and the fund project of Jiangxi Province Education Office (GJJ160529).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Pengming, W., Peng, L., Rui, L., Bin, W. (2017). Leveraging External Knowledge to Enhance Query Model for Event Query. In: Wen, J., Nie, J., Ruan, T., Liu, Y., Qian, T. (eds) Information Retrieval. CCIR 2017. Lecture Notes in Computer Science(), vol 10390. Springer, Cham. https://doi.org/10.1007/978-3-319-68699-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-68699-8_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68698-1
Online ISBN: 978-3-319-68699-8
eBook Packages: Computer ScienceComputer Science (R0)