IMPACT SCORE ESTIMATION WITH PRIVACY PRESERVATION IN INFORMATION RETRIEVAL
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References
Sy, M. F., Ranwez, S., Montmain, J., Regnault, A., Crampes, M., &Ranwez, V. (2012). User centered and ontology based information retrieval system for life sciences. BMC bioinformatics, 13(Suppl 1), S4.
Sagayam, R., Srinivasan, S., &Roshni, S. (2012). A survey of text mining: Retrieval, extraction and indexing techniques. International Journal of Computational Engineering Research, 2(5).
Wu, Q., Burges, C. J., Svore, K. M., &Gao, J. (2010). Adapting boosting for information retrieval measures. Information Retrieval, 13(3), 254-270.
Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information processing & management, 24(5), 513-523.
Carpineto, C., & Romano, G. (2012). A survey of automatic query expansion in information retrieval. ACM Computing Surveys (CSUR), 44(1), 1.
Roy, D., Paul, D., Mitra, M., &Garain, U. (2016). Using word embeddings for automatic query expansion. arXiv preprint arXiv:1606.07608.
Gan, L., & Hong, H. (2015). Improving query expansion for information retrieval using wikipedia. International Journal of Database Theory and Application, 8(3), 27-40.
Cao, G., Nie, J. Y., Gao, J., & Robertson, S. (2008, July). 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.
Lavrenko, V., & Croft, W. B. (2001, September). 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.
Gao, J., &Nie, J. Y. (2012, October). Towards concept-based translation models using search logs for query expansion. In Proceedings of the 21st ACM international conference on Information and knowledge management (p. 1). ACM.
Riezler, S., & Liu, Y. (2010). Query rewriting using monolingual statistical machine translation. Computational Linguistics, 36(3), 569-582.
Robertson, S., & Zaragoza, H. (2009). The probabilistic relevance framework: BM25 and beyond. Foundations and Trends® in Information Retrieval, 3(4), 333-389.
Imhof, M., &Braschler, M. (2017). A study of untrained models for multimodal information retrieval. Information Retrieval Journal, 1-26.
Büttcher, S., Clarke, C. L., &Lushman, B. (2006, August). Term proximity scoring for ad-hoc retrieval on very large text collections. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 621-622). ACM.
He, B., Huang, J. X., & Zhou, X. (2011). Modeling term proximity for probabilistic information retrieval models. Information Sciences, 181(14), 3017-3031.
Van Rijsbergen, C. J. (1977). A theoretical basis for the use of co-occurrence data in information retrieval. Journal of documentation, 33(2), 106-119.
Singh, J., &Sharan, A. (2015, February). Co-occurrence and Semantic Similarity Based Hybrid Approach for Improving Automatic Query Expansion in Information Retrieval. In ICDCIT (pp. 415-418).
Robertson, S. E. (1990). On term selection for query expansion. Journal of documentation, 46(4), 359-364.
Carpineto, C., & Romano, G. (2012). A survey of automatic query expansion in information retrieval. ACM Computing Surveys (CSUR), 44(1), 1.
Bigi, B. (2003, April). Using Kullback-Leibler distance for text categorization. In European Conference on Information Retrieval (pp. 305-319). Springer, Berlin, Heidelberg.
Pérez-Agüera, J. R., & Araujo, L. (2008). Comparing and combining methods for automatic query expansion. arXiv preprint arXiv:0804.2057.
Shaw, J. A., & Fox, E. A. (1995). Combination of multiple searches. NIST SPECIAL PUBLICATION SP, 105-105.
Wei, Z., Gao, W., El-Ganainy, T., Magdy, W., & Wong, K. F. (2014, July). Ranking model selection and fusion for effective microblog search. In Proceedings of the first international workshop on Social media retrieval and analysis (pp. 21-26). ACM.
Singh, J., &Sharan, A. (2017). Rank fusion and semantic genetic notion based automatic query expansion model. Swarm and Evolutionary Computation.
Huang, G., Wang, S., & Zhang, X. (2011). Query expansion based on associated semantic space. Journal of Computers, 6(2), 172-177.
Prieto-Diaz, R., &Arango, G. (1991). Domain analysis and software systems modeling. IEEE Computer Society Press.