Abstract
Since the length of microblog texts, such as tweets, is strictly limited to 140 characters, traditional Information Retrieval techniques usually suffer severely from the vocabulary mismatch problem such that they cannot yield good performance in the context of microblogosphere. To address this critical challenge, in this paper, we propose a new language modeling approach for microblog retrieval by inferring various types of context information. In particular, we expand the query using knowledge terms derived from Freebase so that the expanded one can better reflect the information need. Besides, in order to further answer users’ real-time information need, we incorporate temporal evidences into the expansion methods so that the proposed approach can boost recent tweets in the retrieval results with respect to a given topic. Experimental results on two official TREC Twitter corpora demonstrate the significant superiority of our approach over baseline 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
References
Albakour, M., Macdonald, C., Ounis, I., et al.: On sparsity and drift for effective real-time filtering in microblogs. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 419–428. ACM (2013)
Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O’Reilly Media, Inc., Newton (2009)
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250. ACM (2008)
Cao, G., Nie, J.Y., Gao, J., Robertson, S.: Selecting good expansion terms for pseudo-relevance feedback. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 243–250. ACM (2008)
Dakka, W., Gravano, L., Ipeirotis, P.G.: Answering general time-sensitive queries. IEEE Transactions on Knowledge and Data Engineering 24(2), 220–235 (2012)
Dong, A., Zhang, R., Kolari, P., Bai, J., Diaz, F., Chang, Y., Zheng, Z., Zha, H.: Time is of the essence: improving recency ranking using twitter data. In: Proceedings of the 19th International Conference on World Wide Web, pp. 331–340. ACM (2010)
Kotov, A., Zhai, C.: Tapping into knowledge base for concept feedback: leveraging conceptnet to improve search results for difficult queries. In: Proceedings of the fifth ACM International Conference on Web search and Data Mining, pp. 403–412. ACM (2012)
Lafferty, J., Zhai, C.: Document language models, query models, and risk minimization for information retrieval. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 111–119. ACM (2001)
Li, X., Croft, W.B.: Time-based language models. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, pp. 469–475. ACM (2003)
Li, Y., Luk, W.P.R., Ho, K.S.E., Chung, F.L.K.: Improving weak ad-hoc queries using wikipedia asexternal corpus. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 797–798. ACM (2007)
Liang, F., Qiang, R., Yang, J.: Exploiting real-time information retrieval in the microblogosphere. In: Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 267–276. ACM (2012)
Lin, J., Efron, M.: Overview of the TREC-2013 microblog track. In: Proceedings of TREC. vol. 2013 (2013)
Lv, Y., Zhai, C.: A comparative study of methods for estimating query language models with pseudo feedback. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 1895–1898. ACM (2009)
Miyanishi, T., Seki, K., Uehara, K.: Improving pseudo-relevance feedback via tweet selection. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, pp. 439–448. ACM (2013)
Ounis, I., Macdonald, C., Lin, J., Soboroff, I.: Overview of TREC-2011 microblog track. In: Proceeddings of the 20th Text REtrieval Conference (TREC 2011) (2011)
Pan, D., Zhang, P., Li, J., Song, D., Wen, J.-R., Hou, Y., Hu, B., Jia, Y., De Roeck, A.: Using Dempster-Shafer’s evidence theory for query expansion based on freebase knowledge. In: Banchs, R.E., Silvestri, F., Liu, T.-Y., Zhang, M., Gao, S., Lang, J. (eds.) AIRS 2013. LNCS, vol. 8281, pp. 121–132. Springer, Heidelberg (2013)
Xu, Y., Jones, G.J., Wang, B.: Query dependent pseudo-relevance feedback based on wikipedia. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 59–66. ACM (2009)
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)
Acknowledgments
The work reported in this paper was supported by the National Natural Science Foundation of China Grant 61370116.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Lv, C., Qiang, R., Fan, F., Yang, J. (2015). Knowledge-Based Query Expansion in Real-Time Microblog Search. In: Zuccon, G., Geva, S., Joho, H., Scholer, F., Sun, A., Zhang, P. (eds) Information Retrieval Technology. AIRS 2015. Lecture Notes in Computer Science(), vol 9460. Springer, Cham. https://doi.org/10.1007/978-3-319-28940-3_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-28940-3_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-28939-7
Online ISBN: 978-3-319-28940-3
eBook Packages: Computer ScienceComputer Science (R0)