AQtpUIR: Adaptive query term proximity based user information retrieval

References

• Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern information retrieval (Vol. 463). New York: ACM press.
   Google Scholar
• Croft, B. (2019, July). The Importance of Interaction in Information Retrieval. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1-2). ACM.
   Google Scholar
• Schütze, H., Manning, C. D., & Raghavan, P. (2008, June). Introduction to information retrieval. In Proceedings of the international communication of association for computing machinery conference (p. 260).
   Google Scholar
• Büttcher, S., Clarke, C.L., Lushman, B.: 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, August 2006
   Google Scholar
• White, R. W. (2016). Interactions with search systems. Cambridge University Press.
   Google Scholar
• Croft, W. B., Metzler, D., & Strohman, T. (2010). Search engines: Information retrieval in practice (Vol. 520). Reading: Addison-Wesley.
   Google Scholar
• Rasolofo, Y., Savoy, J.: Term proximity scoring for keyword-based retrieval systems. In: Sebastiani, F. (ed.) ECIR 2003. LNCS, vol. 2633, pp. 207–218. Springer, Heidelberg (2003).
   Google Scholar
• Khennak, I., & Drias, H. (2020). A Novel Hybrid Correlation Measure for Query Expansion-Based Information Retrieval. In Critical Approaches to Information Retrieval Research (pp. 1-19). IGI Global.
   Google Scholar
• Idreos, S., Papaemmanouil, O., Chaudhuri, S.: Overview of data exploration techniques. In: ACM SIGMOD International Conference on Management of Data, pp. 277–281 (2015)
   Google Scholar
• Patel, J., & Singh, V. (2017, December). Query morphing: A proximity-based approach for data exploration and query reformulation. In International Conference on Mining Intelligence and Knowledge Exploration (pp. 261-273). Springer, Cham.
   Google Scholar
• X. Liu and W. B. Croft. Passage retrieval based on language models. In Proceedings of CIKM 2002, pages 375–382, 2002.
   Google Scholar
• Song, Yang, Qinmin Vivian Hu, and Liang He. “Let terms choose their own kernels: An intelligent approach to kernel selection for healthcare search.” Information Sciences 485 (2019): 55-70. doi: 10.1016/j.ins.2019.02.010
   Web of Science ®Google Scholar
• Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information processing & management, 24(5), 513–523. doi: 10.1016/0306-4573(88)90021-0
   Web of Science ®Google Scholar
• Paik, Jiaul H. “A novel TF-IDF weighting scheme for effective ranking.” Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. 2013.
   Google Scholar
• He, B., Huang, J.X., Zhou, X.: Modeling term proximity for probabilistic information retrieval models. Inf. Sci. 181(14), 3017–3031 (2011) doi: 10.1016/j.ins.2011.03.007
   Web of Science ®Google Scholar
• Miao, J., Huang, J.X., Ye, Z.: Proximity-based rocchio’s model for pseudo relevance. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 535–544. ACM, August 2012
   Google Scholar
• Zhao, J., Huang, J.X., Ye, Z.: Modeling term associations for probabilistic information retrieval. ACM Trans. Inform. Syst. (TOIS) 32(2), 7 (2014)
   Google Scholar
• Saracevic, T.: The notion of relevance in information science: everybody knows what relevance is. But, what is it really? Synthesis Lect. Inform. Concepts Retrieval Serv. 8(3), i-109 (2016)
   Google Scholar
• Cummins, R., & O’Riordan, C. (2009, July). Learning in a pairwise term-term proximity framework for information retrieval. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (pp. 251-258).
   Google Scholar
• J. P. Callan. Passage-Level Evidence in Document Retrieval. In W. B. Croft and C. van Rijsbergen, editors, Proceedings of the Seventeenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 302 – 310, Dublin, Ireland, July 1994. Spring-Verlag.
   Google Scholar
• M. Kaszkiel and J. Zobel. Effective ranking with arbitrary passages. Journal of the American Society of Information Science, 52(4):344– 364, 2001. doi: 10.1002/1532-2890(2000)9999:9999<::AID-ASI1075>3.0.CO;2-#
   Web of Science ®Google Scholar
• Barry, C.L.: User-defined relevance criteria: an exploratory study. J. Am. Soc. Inform. Sci. 45(3), 149–159 (1994) doi: 10.1002/(SICI)1097-4571(199404)45:3<149::AID-ASI5>3.0.CO;2-J
   Web of Science ®Google Scholar
• Robertson, Stephen, and Hugo Zaragoza. “The probabilistic relevance framework: BM25 and beyond.” Foundations and Trends® in Information Retrieval 3.4 (2009): 333-389. doi: 10.1561/1500000019
   Google Scholar
• Büttcher, Stefan, and Charles LA Clarke. “Efficiency vs. Effectiveness in Terabyte-Scale Information Retrieval.” TREC. 2005.
   Google Scholar
• He, Ben, and Iadh Ounis. “Term frequency normalisation tuning for BM25 and DFR models.” European Conference on Information Retrieval. Springer, Berlin, Heidelberg, 2005.
   Google Scholar
• F. Song and B. Croft. A general language model for information retrieval. In Proceedings of the 1999 ACM SIGIR Conference on Research and Development in Information Retrieval, pages 279–280, 1999.
   Google Scholar
• G. Salton, J. Allan, and C. Buckley. Approaches to Passage Retrieval in Full Text Information Systems. In Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages49-58, 1993.
   Google Scholar
• Beigbeder, Michel, and Annabelle Mercier. “An information retrieval model using the fuzzy proximity degree of term occurences.” Proceedings of the 2005 ACM symposium on Applied computing. ACM, 2005.
   Google Scholar
• Clarke, Charles LA, Gordon V. Cormack, and Forbes J. Burkowski. “Shortest substring ranking (MultiText experiments for TREC-4).” TREC. Vol. 4. 1995.
   Google Scholar
• Hawking, David, and Paul Thistlewaite. “Proximity operators-so near and yet so far.” Proceedings of the 4th text retrieval conference. 1995.
   Google Scholar
• Singh, V. (2019). Predicting search intent based on in-search context for exploratory search. International Journal of Advanced Pervasive and Ubiquitous Computing (IJAPUC), 11(3), 53-75. doi: 10.4018/IJAPUC.2019070104
   Google Scholar
• Singh, V., & Dave, M. (2019, December). Improving Result Diversity Using Query Term Proximity in Exploratory Search. In International Conference on Big Data Analytics (pp. 67-87). Springer, Cham.
   Google Scholar
• Arroyuelo, Diego, et al. “To index or not to index: Time-space trade-offs for positional ranking functions in search engines.” Information Systems (2019): 101466.
   Google Scholar
• Zhao, J., Yun, Y.: A proximity language model for information retrieval. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 291–298. ACM, July 2009
   Google Scholar
• Song, R., Taylor, M.J., Wen, J.-R., Hon, H.-W., Yu, Y.: Viewing term proximity from a different perspective. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 346–357. Springer, Heidelberg (2008).
   Google Scholar
• Qiao, Ya-nan, Qinghe Du, and Di-fang Wan. “A study on query terms proximity embedding for information retrieval.” International Journal of Distributed Sensor Networks 13.2 (2017): 1550147717694891. doi: 10.1177/1550147717694891
   Web of Science ®Google Scholar
• Pitis, Silviu. “Methods for retrieving alternative contract language using a prototype.” Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law. ACM, 2017.
   Google Scholar
• Veretennikov, Alexander B. “Proximity Full-Text Search by Means of Additional Indexes with Multi-Component Keys: in Pursuit of Optimal Performance.” International Conference on Data Analytics and Management in Data Intensive Domains. Springer, Cham, 2018.
   Google Scholar
• Pan, Min, et al. “An adaptive term proximity based rocchio’s model for clinical decision support retrieval.” BMC Medical Informatics and Decision Making 19.9 (2019): 251.
   Google Scholar
• Schenkel, R., Broschart, A., Hwang, S., Theobald, M., Weikum, G.: Efficient text proximity search. In: Ziviani, N., Baeza-Yates, R. (eds.) SPIRE 2007. LNCS, vol. 4726, pp. 287–299. Springer, Heidelberg (2007)
   Google Scholar
• Svore, K.M., Kanani, P.H., Khan, N.:. How good is a span of terms?: exploiting proximity to improve web retrieval. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 154–161. ACM, July 2010
   Google Scholar
• Arroyuelo, Diego, et al. “To index or not to index: Time-space trade-offs for positional ranking functions in search engines.” Information Systems (2019): 101466.
   Google Scholar
• Barik, T., & Singh, V. (2020, July). Placing Query Term Proximity in Search Context. In International Conference on Machine Learning, Image Processing, Network Security and Data Sciences (pp. 1-16). Springer, Singapore.
   Google Scholar