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Neural or Statistical: An Empirical Study on Language Models for Chinese Input Recommendation on Mobile Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Leveraging External Knowledge to Enhance Query Model for Event Query

Authors : Wang Pengming, Li Peng, Li Rui, Wang Bin

Published in: Information Retrieval

Publisher: Springer International Publishing

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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.

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Footnotes
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 \(\lambda \).
 
Literature
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)
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)
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)
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
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)
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)
7.
Quezada, M., Poblete, B.: Location-aware model for news events in social media. In: The International ACM SIGIR Conference, pp. 935–938 (2015)
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)
9.
Mclachlan, G.J., Krishnan, T.: The EM algorithm and extensions. Biometrics 382(1), 154–156 (2008)MATH
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)
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)
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)CrossRef
Metadata
Title
Leveraging External Knowledge to Enhance Query Model for Event Query
Authors
Wang Pengming
Li Peng
Li Rui
Wang Bin
Copyright Year
2017
Book
Information Retrieval

Print ISBN: 978-3-319-68698-1

Electronic ISBN: 978-3-319-68699-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-68699-8_18