Relevant bibliographies by topics / Pseudo relevance feedback

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Pseudo relevance feedback'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Pseudo relevance feedback"

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Zhou, Dong, Mark Truran, Jianxun Liu, and Sanrong Zhang. "Collaborative pseudo-relevance feedback." Expert Systems with Applications 40, no. 17 (2013): 6805–12. http://dx.doi.org/10.1016/j.eswa.2013.06.030.

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Chen, Zhaofeng, Naixuan Guo, Jiu Sun, et al. "Pseudo-Relevance Feedback Method Based on the Topic Relevance Model." Mathematical Problems in Engineering 2022 (July 7, 2022): 1–6. http://dx.doi.org/10.1155/2022/1697950.

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In the field of information retrieval, most pseudo-relevance feedback models select candidate terms from the top k documents returned by the first-pass retrieval, but they cannot identify the reliability of these documents. This paper proposed a new approach to obtain feedback information more comprehensively by constructing four corresponding models. Firstly, the algorithm incorporated topic-based relevance information into the relevance model RM3 and constructed a topic-based relevance model, denoted as TopRM3, with two corresponding variants. TopRM3 estimated the reliability of a feedback d
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Parapar, Javier, Manuel A. Presedo-Quindimil, and Álvaro Barreiro. "Score distributions for Pseudo Relevance Feedback." Information Sciences 273 (July 2014): 171–81. http://dx.doi.org/10.1016/j.ins.2014.03.034.

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Sakai, Tetsuya, Toshihiko Manabe, and Makoto Koyama. "Flexible pseudo-relevance feedback via selective sampling." ACM Transactions on Asian Language Information Processing 4, no. 2 (2005): 111–35. http://dx.doi.org/10.1145/1105696.1105699.

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Chen, Lin, Lin Chun, Lin Ziyu, and Zou Quan. "Hybrid pseudo-relevance feedback for microblog retrieval." Journal of Information Science 39, no. 6 (2013): 773–88. http://dx.doi.org/10.1177/0165551513487846.

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Zhong Minjuan, and Wan Changxuan. "Pseudo-Relevance Feedback Driven for XML Query Expansion." Journal of Convergence Information Technology 5, no. 9 (2010): 146–56. http://dx.doi.org/10.4156/jcit.vol5.issue9.15.

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Mosbah, Mawloud, and Bachir Boucheham. "Pseudo relevance feedback based on majority voting mechanism." International Journal of Web Science 3, no. 1 (2017): 58. http://dx.doi.org/10.1504/ijws.2017.088688.

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Mosbah, Mawloud, and Bachir Boucheham. "Pseudo relevance feedback based on majority voting mechanism." International Journal of Web Science 3, no. 1 (2017): 58. http://dx.doi.org/10.1504/ijws.2017.10009576.

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Na, Seung-Hoon, and Kangil Kim. "Verbosity normalized pseudo-relevance feedback in information retrieval." Information Processing & Management 54, no. 2 (2018): 219–39. http://dx.doi.org/10.1016/j.ipm.2017.09.006.

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Keikha, Andisheh, Faezeh Ensan, and Ebrahim Bagheri. "Query expansion using pseudo relevance feedback on wikipedia." Journal of Intelligent Information Systems 50, no. 3 (2017): 455–78. http://dx.doi.org/10.1007/s10844-017-0466-3.

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Dissertations / Theses on the topic "Pseudo relevance feedback"

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Billerbeck, Bodo, and bodob@cs rmit edu au. "Efficient Query Expansion." RMIT University. Computer Science and Information Technology, 2006. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20060825.154852.

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Hundreds of millions of users each day search the web and other repositories to meet their information needs. However, queries can fail to find documents due to a mismatch in terminology. Query expansion seeks to address this problem by automatically adding terms from highly ranked documents to the query. While query expansion has been shown to be effective at improving query performance, the gain in effectiveness comes at a cost: expansion is slow and resource-intensive. Current techniques for query expansion use fixed values for key parameters, determined by tuning on test collections
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Deveaud, Romain. "Vers une représentation du contexte thématique en Recherche d'Information." Phd thesis, Université d'Avignon, 2013. http://tel.archives-ouvertes.fr/tel-00918877.

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Quand des humains cherchent des informations au sein de bases de connaissancesou de collections de documents, ils utilisent un système de recherche d'information(SRI) faisant office d'interface. Les utilisateurs doivent alors transmettre au SRI unereprésentation de leur besoin d'information afin que celui-ci puisse chercher des documentscontenant des informations pertinentes. De nos jours, la représentation du besoind'information est constituée d'un petit ensemble de mots-clés plus souvent connu sousla dénomination de " requête ". Or, quelques mots peuvent ne pas être suffisants pourreprésente
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Htait, Amal. "Sentiment analysis at the service of book search." Electronic Thesis or Diss., Aix-Marseille, 2019. http://www.theses.fr/2019AIXM0260.

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Le Web est en croissance continue, et une quantité énorme de données est générée par les réseaux sociaux, permettant aux utilisateurs d'échanger une grande diversité d'informations. En outre, les textes au sein des réseaux sociaux sont souvent subjectifs. L'exploitation de cette subjectivité présente au sein des textes peut être un facteur important lors d'une recherche d'information. En particulier, cette thèse est réalisée pour répondre aux besoins de la plate-forme Books de Open Edition en matière d'amélioration de la recherche et la recommandation de livres, en plusieurs langues. La platef
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Lee, Chia-Jung, and 李佳容. "A Block-based Pseudo Relevance Feedback Algorithm for Image Retrieval." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/18200220613383670902.

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碩士<br>國立中央大學<br>資訊管理學系<br>101<br>Nowadays the network has become one of the important ways to obtain information. Therefore, it is important to effectively search for information. For image search, CBIR (Content-Based Image Retrieval) is major technique. However, the semantic gap problem limits the performance of CBIR systems. In literature, RF (Relevance Feedback) can be used to improve the retrieval performance of CBIR systems. It is usually based on asking users to give feedbacks, and the retrieval results are re-ranked. One major limitation of RF is the need of the user in the loop process
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Wu, Ji Wei, and 吳智瑋. "Improving Information Retrieval Performance by an Enhanced Pseudo Relevance Feedback Algorithm." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/58994995075025603190.

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碩士<br>中華大學<br>資訊工程學系(所)<br>96<br>Owing to the rapid growth and popularization of Internet and information technology, information retrieval systems has become a necessary part of our modern life. Users find valuable information from either digital libraries or the Internet by a few keywords or a nature language sentence. However, the quality of an information retrieval system relies heavily on the accuracy of the information retrieved. The retrieved information should be not only matched the user’s query, but also ranked well according to its relevance to the user’s query. In the literatures,
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陳憶文. "Exploring Effective Pseudo-Relevance Feedback and Proximity Information for Speech Retrieval and Transcription." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/24695216658836083699.

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碩士<br>國立臺灣師範大學<br>資訊工程學系<br>101<br>Pseudo-relevance feedback is by far the most commonly-used paradigm for query reformulation in spoken document retrieval, which assumes that a small amount of top-ranked feedback documents obtained from the initial retrieval are relevant and can be utilized for query expansion. Nevertheless, simply taking all of the top-ranked feedback documents acquired from the initial retrieval for query modeling does not necessary work well, especially when the top-ranked documents contain much redundant or non-relevant cues. In view of this, we explore different kinds of
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陳俊諭. "A Study on Integrating Document Relatedness and Query Clarity Information for Improved Pseudo-Relevance Feedback." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/07913424667955135602.

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碩士<br>國立臺灣師範大學<br>資訊工程學系<br>102<br>Pseudo-relevant document selection figures prominently in query reformulation with pseudo-relevance feedback (PRF) for an information retrieval (IR) system. Most of conventional IR systems select pseudo-relevant documents for query reformulation simply based on the query-document relevance scores returned by the initial round of retrieval. In this thesis, we propose a novel method for pseudo-relevant document selection that considers not only the query-document relevance scores but also the relatedness cues among documents. To this end, we adopt and formalize
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Book chapters on the topic "Pseudo relevance feedback"

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Yan, Rong, and Guanglai Gao. "Pseudo Topic Analysis for Boosting Pseudo Relevance Feedback." In Web and Big Data. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26072-9_26.

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Yan, Rong, Alexander G. Hauptmann, and Rong Jin. "Pseudo-Relevance Feedback for Multimedia Retrieval." In Video Mining. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-6928-9_11.

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Yan, Rong, Alexander Hauptmann, and Rong Jin. "Multimedia Search with Pseudo-relevance Feedback." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45113-7_24.

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Raman, Karthik, Raghavendra Udupa, Pushpak Bhattacharya, and Abhijit Bhole. "On Improving Pseudo-Relevance Feedback Using Pseudo-Irrelevant Documents." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12275-0_50.

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Whiting, Stewart, Iraklis A. Klampanos, and Joemon M. Jose. "Temporal Pseudo-relevance Feedback in Microblog Retrieval." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28997-2_55.

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Wu, Yuanbin, Qi Zhang, Yaqian Zhou, and Xuanjing Huang. "Pseudo-Relevance Feedback Based on mRMR Criteria." In Information Retrieval Technology. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17187-1_20.

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Tanioka, Hiroki. "Pseudo Relevance Feedback Using Fast XML Retrieval." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03761-0_22.

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Geng, Bin, Fang Zhou, Jiao Qu, Bo-Wen Zhang, Xiao-Ping Cui, and Xu-Cheng Yin. "Social Book Search with Pseudo-Relevance Feedback." In Neural Information Processing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12640-1_25.

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Ariannezhad, Mozhdeh, Ali Montazeralghaem, Hamed Zamani, and Azadeh Shakery. "Iterative Estimation of Document Relevance Score for Pseudo-Relevance Feedback." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56608-5_65.

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Jalali, Vahid, and Mohammad Reza Matash Borujerdi. "Concept Based Pseudo Relevance Feedback in Biomedical Field." In Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01203-7_6.

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Conference papers on the topic "Pseudo relevance feedback"

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Lv, Yuanhua, and ChengXiang Zhai. "Positional relevance model for pseudo-relevance feedback." In Proceeding of the 33rd international ACM SIGIR conference. ACM Press, 2010. http://dx.doi.org/10.1145/1835449.1835546.

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Clough, Paul, and Mark Sanderson. "Measuring pseudo relevance feedback & CLIR." In the 27th annual international conference. ACM Press, 2004. http://dx.doi.org/10.1145/1008992.1009082.

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Pu, Qiang, and Daqing He. "Pseudo relevance feedback using semantic clustering in relevance language model." In Proceeding of the 18th ACM conference. ACM Press, 2009. http://dx.doi.org/10.1145/1645953.1646268.

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Sakai, Tetsuya, and Stephen E. Robertson. "Flexible pseudo-relevance feedback using optimization tables." In the 24th annual international ACM SIGIR conference. ACM Press, 2001. http://dx.doi.org/10.1145/383952.384035.

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Ganguly, Debasis, Johannes Leveling, Walid Magdy, and Gareth J. F. Jones. "Patent query reduction using pseudo relevance feedback." In the 20th ACM international conference. ACM Press, 2011. http://dx.doi.org/10.1145/2063576.2063863.

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Keikha, Mostafa, Jangwon Seo, W. Bruce Croft, and Fabio Crestani. "Predicting document effectiveness in pseudo relevance feedback." In the 20th ACM international conference. ACM Press, 2011. http://dx.doi.org/10.1145/2063576.2063890.

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Miyanishi, Taiki, Kazuhiro Seki, and Kuniaki Uehara. "Improving pseudo-relevance feedback via tweet selection." In the 22nd ACM international conference. ACM Press, 2013. http://dx.doi.org/10.1145/2505515.2505701.

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Montazeralghaem, Ali, Hamed Zamani, and Azadeh Shakery. "Term Proximity Constraints for Pseudo-Relevance Feedback." In SIGIR '17: The 40th International ACM SIGIR conference on research and development in Information Retrieval. ACM, 2017. http://dx.doi.org/10.1145/3077136.3080728.

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Zamani, Hamed, Javid Dadashkarimi, Azadeh Shakery, and W. Bruce Croft. "Pseudo-Relevance Feedback Based on Matrix Factorization." In CIKM'16: ACM Conference on Information and Knowledge Management. ACM, 2016. http://dx.doi.org/10.1145/2983323.2983844.

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He, Tingting, and Xionglu Dai. "Pseudo-relevance feedback query based on Wikipedia." In 2012 IEEE International Conference on Granular Computing (GrC-2012). IEEE, 2012. http://dx.doi.org/10.1109/grc.2012.6468659.

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