Abstract
User-generated content, such as web pages, is often annotated by users with free-text labels, called annotations, which can be an effective source of information for query formulation tasks. The implicit relationships between annotations can be important to select expansion terms. However, extracting such knowledge from social annotations presents many challenges, since annotations are often ambiguous, noisy, and uncertain. Besides, most research uses a single query expansion method for query expansion tasks, and never considers the annotations attributes. In contrast, in this paper, we proposed a novel framework that optimized the combination of three query expansion methods used for expansion terms from social annotations in three strategies. Furthermore, we also introduce learning to rank methods for phrase weighting, and select the features from social annotation resource for training ranking model. Experimental results on three TREC test collections show that the retrieval performance can be improved by our proposed method.
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References
Borrego, A., Fry, J.: Measuring researchers’ use of scholarly information through social bookmarking data: a case study of BibSonomy. J. Inf. Sci. 38(3), 297–308 (2012)
Heymann, P., Koutrika, G., Molina, H.G.: Can social bookmarking improve web search? In: Proceedings of the International Conference on Web Search and Data Mining, pp. 195–205 (2008)
Bouadjenek, M.R., Hacid, H., Bouzeghoub, M., Vakali, A.: Using social annotations to enhance document representation for personalized search. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1049–1052. ACM (2013)
Laura, G.M.: Social bookmarking, folksonomies, and web 2.0 tools. Searcher 14(6), 26–38 (2006)
Kiu, C., Tsui, E.: TaxoFolk: a hybrid taxonomy-folksonomy structure for knowledge classification and navigation. Expert Syst. Appl. 38(5), 6049–6058 (2011)
Xu, S., Bao, S., Fei, B., Su, Z., Yu, Y.: Exploring folksonomy for personalized search. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 155–162. ACM (2008)
Lin, Y., Lin, H., Jin, S., Ye, Z.: Social annotation in query expansion: a machine learning approach. In: SIGIR 2011: Proceedings of the 34st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 405–414. ACM (2011)
Guo, Q., Liu, W., Lin, Y., Lin, H.: Query expansion based on user quality in folksonomy. In: Hou, Y., Nie, J.-Y., Sun, L., Wang, B., Zhang, P. (eds.) AIRS 2012. LNCS, vol. 7675, pp. 396–405. Springer, Heidelberg (2012). doi:10.1007/978-3-642-35341-3_35
Bendersky, M., Fisher, D., Croft, W.B.: UMass at TREC 2010 web track: term dependence, spam filtering and quality bias. In: Proceedings of TREC-10 (2011)
Yuvarani, M., Lyengar, N., Kannan, A.: Improved concept-based query expansion using Wikipedia. Int. J. Commun. Netw. Distrib. Syst. 11(17), 26–41 (2013)
Diaz, F., Metzler, D.: Improving the estimation of relevance models using large external corpora. In: Proceedings of the 29st Annual International ACM SIGIR Conference on Research and Development in Information retrieval, pp. 154–161. ACM (2006)
Liu, T.: Learning to rank for information retrieval. Found. Trends Inf. Retrieval 3(3), 225–331 (2009)
Lin, Y., Lin, H., Wu, J., Xu, K.: Learning to rank with cross entropy. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM 2011), pp. 2057–2060. ACM (2011)
Joachims, T.: Optimizing search engines using click through data. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133–142. ACM (2002)
Cao, Z., Qin, T., Liu, T.Y., Tsai, M.F., Li, H.: Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the ICML, pp. 129–136. ACM (2007)
Wu, Q., Burges, C., Svore, K., Gao, J.: Ranking, boosting and model adaptation. Technical report, Microsoft Technical Report MSR-TR-2008-109 (2008)
Lu, C., Hu, X., Chen, X., Park, J.R.: The Topic-perspective model for folksonomy systems. In: Proceeding of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 683–691 (2010)
Acknowledgments
This work is partially supported by grant from the Natural Science Foundation of China (No. 61402075, 61602078, 61572102, 61632011), the Ministry of Education Humanities and Social Science Project (No. 16YJCZH12), the Fundamental Research Funds for the Central Universities (No. DUT17RC(3)016).
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Lin, Y., Xu, B., Li, L., Lin, H., Xu, K. (2017). Social Annotation for Query Expansion Learning from Multiple Expansion Strategies. In: Cheng, X., Ma, W., Liu, H., Shen, H., Feng, S., Xie, X. (eds) Social Media Processing. SMP 2017. Communications in Computer and Information Science, vol 774. Springer, Singapore. https://doi.org/10.1007/978-981-10-6805-8_15
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DOI: https://doi.org/10.1007/978-981-10-6805-8_15
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