Abstract
Learning to rank, which can fuse various of features, performs well in microblog retrieval. However, it is still unclear how the features function in microblog ranking. To address this issue, this paper examines the contribution of each single feature together with the contribution of the feature combinations via the ranking SVM for microblog retrieval modeling. The experimental results on the TREC microblog collection show that textual features, i.e. content relevance between a query and a microblog, contribute most to the retrieval performance. And the combination of certain non-textual features and textual features can further enhance the retrieval performance, though non-textual features alone produce rather weak results.
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References
Duan, Y., Jiang, L., Qin, T., Zhou, M., Shum, H.Y.: An Empirical Study on Learning to Rank of Tweets. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 295–303. Association for Computational Linguistics, Beijing (2010)
Metzler, D., Cai, C.: USC/ISI at TREC 2011: Microblog Track. In: Proceeding of the Twentieth Text REtrieval Conference. NIST, Gaithersburg (2011)
Han, Z., Li, X., Yang, M., Qi, H., Li, S., Zhao, T.: HIT at TREC 2012 Microblog Track. In: Proceeding of the Twenty-First Text REtrieval Conference. NIST, Gaithersburg (2012)
Zhang, X., Lu, S., He, B., Xu, J., Luo, T.: UCAS at TREC-2012 Microblog Track. In: Proceeding of the Twenty-First Text REtrieval Conference. NIST, Gaithersburg (2012)
Zhu, B., Gao, J., Han, X., Shi, C., Liu, S., Liu, Y., Cheng, X.: ICTNET at Microblog Track TREC 2012. In: Proceeding of the Twenty-First Text REtrieval Conference. NIST, Gaithersburg (2012)
Liang, F., Qiang, R., Hong, Y., Fei, Y., Yang, J.: PKUICST at TREC 2012 Microblog Track. In: Proceeding of the Twenty-First Text REtrieval Conference. NIST, Gaithersburg (2012)
Berendsen, R., Meij, E., Odijk, D., de Rijke, M., Weerkamp, W.: The University of Amsterdam at TREC 2012. In: Proceeding of the Twenty-First Text REtrieval Conference. NIST, Gaithersburg (2012)
Miyanishi, T., Okamura, N., Liu, X., Seki, K., Uehara, K.: Trec 2011 Microblog Track Experiments at Kobe University. In: Proceeding of the Twentieth Text REtrieval Conference. NIST, Gaithersburg (2011)
Lavrenko, V., Croft, W.B.: Relevance-Based Language Models. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 120–127. ACM, New York (2001)
Tao, T., Wang, X., Mei, Q., Zhai, C.: Language Model Information Retrieval with Document Expansion. In: Proceedings of the Main Conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, pp. 407–414. Association for Computational Linguistics, New York (2006)
Ounis, I., Macdonald, C., Lin, J., Soboro, I.: Overview of the TREC-2011 Microblog Track. In: Proceeding of the Twentieth Text REtrieval Conference. NIST, Gaithersburg (2011)
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Han, Z., Li, X., Yang, M., Qi, H., Li, S. (2013). Feature Analysis in Microblog Retrieval Based on Learning to Rank. In: Zhou, G., Li, J., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2013. Communications in Computer and Information Science, vol 400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41644-6_40
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DOI: https://doi.org/10.1007/978-3-642-41644-6_40
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