Relevant bibliographies by topics / Pseudo relevance feedback

Academic literature on the topic 'Pseudo relevance feedback'

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Published: 4 June 2021
Last updated: 11 February 2022

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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 (December 2013): 6805–12. http://dx.doi.org/10.1016/j.eswa.2013.06.030.

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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 (June 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 (May 23, 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 (November 30, 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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Roussinov, Dmitri, and Gheorghe Muresan. "Query expansion: Internet mining vs. pseudo relevance feedback." Proceedings of the American Society for Information Science and Technology 44, no. 1 (October 24, 2008): 1–11. http://dx.doi.org/10.1002/meet.1450440271.

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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 (May 17, 2017): 455–78. http://dx.doi.org/10.1007/s10844-017-0466-3.

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

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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. We show that these parameters may not be generally applicable, and, more significantly, that the assumption that the same parameter settings can be used for all queries is invalid. Using detailed experiments, we demonstrate that new methods for choosing parameters must be found. In conventional approaches to query expansion, the additional terms are selected from highly ranked documents returned from an initial retrieval run. We demonstrate a new method of obtaining expansion terms, based on past user queries that are associated with documents in the collection. The most effective query expansion methods rely on costly retrieval and processing of feedback documents. We explore alternative methods for reducing query-evaluation costs, and propose a new method based on keeping a brief summary of each document in memory. This method allows query expansion to proceed three times faster than previously, while approximating the effectiveness of standard expansion. We investigate the use of document expansion, in which documents are augmented with related terms extracted from the corpus during indexing, as an alternative to query expansion. The overheads at query time are small. We propose and explore a range of corpus-based document expansion techniques and compare them to corpus-based query expansion on TREC data. These experiments show that document expansion delivers at best limited benefits, while query expansion � including standard techniques and efficient approaches described in recent work � usually delivers good gains. We conclude that document expansion is unpromising, but it is likely that the efficiency of query expansion can be further improved.
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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 pourreprésenter précisément et efficacement l'état cognitif complet d'un humain par rapportà son besoin d'information initial. Sans une certaine forme de contexte thématiquecomplémentaire, le SRI peut ne pas renvoyer certains documents pertinents exprimantdes concepts n'étant pas explicitement évoqués dans la requête.Dans cette thèse, nous explorons et proposons différentes méthodes statistiques, automatiqueset non supervisées pour la représentation du contexte thématique de larequête. Plus spécifiquement, nous cherchons à identifier les différents concepts implicitesd'une requête formulée par un utilisateur sans qu'aucune action de sa part nesoit nécessaire. Nous expérimentons pour cela l'utilisation et la combinaison de différentessources d'information générales représentant les grands types d'informationauxquels nous sommes confrontés quotidiennement sur internet. Nous tirons égalementparti d'algorithmes de modélisation thématique probabiliste (tels que l'allocationde Dirichlet latente) dans le cadre d'un retour de pertinence simulé. Nous proposonspar ailleurs une méthode permettant d'estimer conjointement le nombre de conceptsimplicites d'une requête ainsi que l'ensemble de documents pseudo-pertinent le plusapproprié afin de modéliser ces concepts. Nous évaluons nos approches en utilisantquatre collections de test TREC de grande taille. En annexes, nous proposons égalementune approche de contextualisation de messages courts exploitant des méthodesde recherche d'information et de résumé automatique
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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 plateforme offre des informations générées par des utilisateurs, riches en sentiments. Par conséquent, l'analyse précédente, concernant l'exploitation de sentiment en recherche d'information, joue un rôle important dans cette thèse et peut servir l'objectif d'une amélioration de qualité de la recherche de livres en utilisant les informations générées par les utilisateurs. Par conséquent, nous avons choisi de suivre une voie principale dans cette thèse consistant à combiner les domaines analyse de sentiment (AS) et recherche d'information (RI), dans le but d'améliorer les suggestions de la recherche de livres. Nos objectifs peuvent être résumés en plusieurs points: • Une approche d'analyse de sentiment, facilement applicable sur différentes langues, peu coûteuse en temps et en données annotées. • De nouvelles approches pour l'amélioration de la qualité lors de la recherche de livres, basées sur l'utilisation de l'analyse de sentiment dans le filtrage, l'extraction et la classification des informations
The web technology is in an on going growth, and a huge volume of data is generated in the social web, where users would exchange a variety of information. In addition to the fact that social web text may be rich of information, the writers are often guided by provoked sentiments reflected in their writings. Based on that concept, locating sentiment in a text can play an important role for information extraction. The purpose of this thesis is to improve the book search and recommendation quality of the Open Edition's multilingual Books platform. The Books plat- form also offers additional information through users generated information (e.g. book reviews) connected to the books and rich in emotions expressed in the users' writings. Therefore, the previous analysis, concerning locating sentiment in a text for information extraction, plays an important role in this thesis, and can serve the purpose of quality improvement concerning book search, using the shared users generated information. Accordingly, we choose to follow a main path in this thesis to combine sentiment analysis (SA) and information retrieval (IR) fields, for the purpose of improving the quality of book search. Two objectives are summarised in the following, which serve the main purpose of the thesis in the IR quality improvement using SA: • An approach for SA prediction, easily applicable on different languages, low cost in time and annotated data. • New approaches for book search quality improvement, based on SA employment in information filtering, retrieving and classifying
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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
國立中央大學
資訊管理學系
101
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. To this end, PRF (Pseudo Relevance Feedback) was proposed that considers top-k images as the pseudo feedbacks to re-rank the retrieval results. This thesis proposes an algorithm called Block-Based Pseudo Relevance Feedback (BBPRF) to improve the traditional PRF approach. The idea of this algorithm is to assign higher weights to higher ranked images. Particularly, top-k images as the feedbacks are divided into two to k blocks and each block has a specific weight, so the weighted feedbacks will benefit the next feedback iteration. The experiments are based on the NUS-WIDE and Caltech256 datasets and the Rocchio algorithm is used as the traditional feedback algorithm. The first experimental results show that our proposed BBPRF performs better than the traditional PRF approach in terms of precision at 10, 20, and 50. In particularly, using top 30 images with 30 blocks perform the best. The second study further integrates the user’s feedbacks and BBPRF, and the retrieval performance is even better than using BBPRF alone.
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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
中華大學
資訊工程學系(所)
96
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, researchers found that Relevance Feedback (RF) information is quite useful for an information retrieval system to improve its accuracy. Among the proposed relevance feedback algorithms, the standard Rocchio’s relevance feedback algorithm is the most well-known and widely employed in information retrieval systems. Furthermore, the idea of pseudo relevance feedback was proposed for the relevance feedback algorithms. It reduces user’s burden by deciding automatically relevant and irrelevant documents according to the ranks of the retrieval results. Although relevance feedback algorithms can be used to improve retrieval performance, they do not discriminate well the degree of importance on either documents or terms. To cope with this problem, an enhanced pseudo relevance feedback algorithm is proposed in this thesis. Experimental results showed that the performance of the proposed algorithm outperforms the standard Rocchio’s relevance feedback algorithm.
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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
國立臺灣師範大學
資訊工程學系
101
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 information cues for selecting helpful feedback documents to further improve information retrieval. On the other hand, relevance model (RM) based on “bag-of-words” assumption, which can facilitate the derivation and estimation, may be oversimplified for the task of language modeling in speech recognition. Hence, we also enhance RM in two significant aspects. First, “bag-of-words” assumption of RM is relaxed by incorporating word proximity information into RM formulation. Second, topic-based proximity information is additionally explored to further enhance the proximity-based RM framework. Experiments conducted on not only a spoken document retrieval task but also a speech recognition task indicates that our approaches can bring competitive utilities to existing ones.
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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
國立臺灣師範大學
資訊工程學系
102
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 the notion of Markov random walk (MRW) to glean the relatedness cues among documents, which in turn can be used in concert with the query-document relevance scores to select representative documents for PRF. Furthermore, on top of the language modeling (LM) framework for IR, we also investigate how to effectively combine the original query model and new query model estimated from the selected pseudo-relevant documents in a more effective manner by virtue of the so-called query clarity measure. A series of experiments conducted on both the TDT (Topic Detection and Tracking) collection and the WSJ (Wall Street Journal) collection seem to demonstrate the performance merits of our proposed methods.
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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, 345–61. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26072-9_26.

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Yan, Rong, Alexander Hauptmann, and Rong Jin. "Multimedia Search with Pseudo-relevance Feedback." In Lecture Notes in Computer Science, 238–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45113-7_24.

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Yan, Rong, Alexander G. Hauptmann, and Rong Jin. "Pseudo-Relevance Feedback for Multimedia Retrieval." In Video Mining, 309–38. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-6928-9_11.

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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, 573–76. Berlin, Heidelberg: 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, 522–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28997-2_55.

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Tanioka, Hiroki. "Pseudo Relevance Feedback Using Fast XML Retrieval." In Lecture Notes in Computer Science, 218–23. Berlin, Heidelberg: 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, 203–11. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12640-1_25.

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Wu, Yuanbin, Qi Zhang, Yaqian Zhou, and Xuanjing Huang. "Pseudo-Relevance Feedback Based on mRMR Criteria." In Information Retrieval Technology, 211–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17187-1_20.

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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, 676–83. Cham: 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, 69–79. Berlin, Heidelberg: 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. New York, New York, USA: 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. New York, New York, USA: 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. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1645953.1646268.

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Whiting, Stewart, Yashar Moshfeghi, and Joemon M. Jose. "Exploring term temporality for pseudo-relevance feedback." In the 34th international ACM SIGIR conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2009916.2010141.

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Aljubran, Murtadha. "Evaluation of Pseudo-Relevance Feedback using Wikipedia." In NLPIR 2019: 2019 the 3rd International Conference on Natural Language Processing and Information Retrieval. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3342827.3342845.

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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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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. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2983323.2983844.

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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. New York, New York, USA: 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. New York, New York, USA: 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. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2063576.2063890.

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