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Journal articles on the topic 'Pseudo relevance feedback'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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 (2008): 1–11. http://dx.doi.org/10.1002/meet.1450440271.

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12

HAN, Kyoung-Soo. "Dualized Topic-Preserving Pseudo Relevance Feedback for Question Answering." IEICE Transactions on Information and Systems E100.D, no. 7 (2017): 1550–53. http://dx.doi.org/10.1587/transinf.2017edl8017.

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13

Tanuwijaya, Evan, Safri Adam, Mohammad Fatoni Anggris, and Agus Zainal Arifin. "Query Expansion menggunakan Word Embedding dan Pseudo Relevance Feedback." Register: Jurnal Ilmiah Teknologi Sistem Informasi 5, no. 1 (2019): 47. http://dx.doi.org/10.26594/register.v5i1.1385.

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Kata kunci merupakan hal terpenting dalam mencari sebuah informasi. Penggunaan kata kunci yang tepat menghasilkan informasi yang relevan. Saat penggunaannya sebagai query, pengguna menggunakan bahasa yang alami, sehingga terdapat kata di luar dokumen jawaban yang telah disiapkan oleh sistem. Sistem tidak dapat memproses bahasa alami secara langsung yang dimasukkan oleh pengguna, sehingga diperlukan proses untuk mengolah kata-kata tersebut dengan mengekspansi setiap kata yang dimasukkan pengguna yang dikenal dengan Query Expansion (QE). Metode QE pada penelitian ini menggunakan Word Embedding k

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14

Kim, Chul-Won, and Sun Park. "Document Summarization using Pseudo Relevance Feedback and Term Weighting." Journal of the Korean Institute of Information and Communication Engineering 16, no. 3 (2012): 533–40. http://dx.doi.org/10.6109/jkiice.2012.16.3.533.

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15

Dang, Edward Kai FUNG, Robert Wing Pong Luk, and James Allan. "Fast Forward Index Methods for Pseudo-Relevance Feedback Retrieval." ACM Transactions on Information Systems 33, no. 4 (2015): 1–33. http://dx.doi.org/10.1145/2744199.

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16

Martínez-Santiago, Fernando, Miguel A. García-Cumbreras, and L. Alfonso Ureña-Lòpez. "Does pseudo-relevance feedback improve distributed information retrieval systems?" Information Processing & Management 42, no. 5 (2006): 1151–62. http://dx.doi.org/10.1016/j.ipm.2006.01.003.

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17

Atwan, Jaffar, Masnizah Mohd, Hasan Rashaideh, and Ghassan Kanaan. "Semantically enhanced pseudo relevance feedback for Arabic information retrieval." Journal of Information Science 42, no. 2 (2015): 246–60. http://dx.doi.org/10.1177/0165551515594722.

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18

Boteanu, Bogdan, Ionuţ Mironică, and Bogdan Ionescu. "Pseudo-relevance feedback diversification of social image retrieval results." Multimedia Tools and Applications 76, no. 9 (2016): 11889–916. http://dx.doi.org/10.1007/s11042-016-3678-6.

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19

Takeuchi, Shin'ichi, Komei Sugiura, Yuhei Akahoshi, and Koji Zettsu. "Spatio-temporal pseudo relevance feedback for scientific data retrieval." IEEJ Transactions on Electrical and Electronic Engineering 12, no. 1 (2016): 124–31. http://dx.doi.org/10.1002/tee.22352.

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20

El Mahdaouy, Abdelkader, Saïd Ouatik El Alaoui, and Eric Gaussier. "Word-embedding-based pseudo-relevance feedback for Arabic information retrieval." Journal of Information Science 45, no. 4 (2018): 429–42. http://dx.doi.org/10.1177/0165551518792210.

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Pseudo-relevance feedback (PRF) is a very effective query expansion approach, which reformulates queries by selecting expansion terms from top k pseudo-relevant documents. Although standard PRF models have been proven effective to deal with vocabulary mismatch between users’ queries and relevant documents, expansion terms are selected without considering their similarity to the original query terms. In this article, we propose a method to incorporate word embedding (WE) similarity into PRF models for Arabic information retrieval (IR). The main idea is to select expansion terms using their dist

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21

Dang, Edward Kai Fung, Robert Wing Pong Luk, and Qing Li. "A Study of Word Bigrams for Pseudo-relevance Feedback in Information Retrieval." JUCS - Journal of Universal Computer Science 30, no. (11) (2024): 1511–28. https://doi.org/10.3897/jucs.112725.

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Traditional information retrieval models mostly adopt a term independence assumption and are based on single terms or unigrams. Past efforts have attempted to go beyond this assumption, such as by using contiguous terms (i.e. word n-grams) or terms appearing in proximity. One such approach employs pseudo-relevance feedback (PRF) in an extended BM25 model, with an expanded query containing bigrams and proximity word pairs besides unigrams. However, the benefit of this approach over the traditional unigram PRF remains inconclusive. We speculate the uncertain effectiveness of bigram PRF in this p

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22

Lehtokangas, Raija, Heikki Keskustalo, and Kalervo Järvelin. "Experiments with transitive dictionary translation and pseudo-relevance feedback using graded relevance assessments." Journal of the American Society for Information Science and Technology 59, no. 3 (2008): 476–88. http://dx.doi.org/10.1002/asi.20762.

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23

Park, Sun, ByungRae Cha, and JangWoo Kwon. "Personalized Document Summarization Using Pseudo Relevance Feedback and Semantic Feature." IETE Journal of Research 58, no. 2 (2012): 155. http://dx.doi.org/10.4103/0377-2063.96182.

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24

Lakshmi, R. Jothi. "Term Selection Methods for Query Expansion in Pseudo Relevance Feedback." Asian Journal of Research in Social Sciences and Humanities 10, no. 10 (2020): 25–34. http://dx.doi.org/10.5958/2249-7315.2020.00019.2.

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25

Kumar Shukla, Abhishek, and Sujoy Das. "Deep Neural Network and Pseudo Relevance Feedback Based Query Expansion." Computers, Materials & Continua 71, no. 2 (2022): 3557–70. http://dx.doi.org/10.32604/cmc.2022.022411.

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26

Ma, Handong, Jiawei Hou, Chenxu Zhu, et al. "QA4PRF: A Question Answering Based Framework for Pseudo Relevance Feedback." IEEE Access 9 (2021): 139303–14. http://dx.doi.org/10.1109/access.2021.3118600.

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27

Xu, Bo, Hongfei Lin, Yuan Lin, Liang Yang, and Kan Xu. "Improving Pseudo-Relevance Feedback With Neural Network-Based Word Representations." IEEE Access 6 (2018): 62152–65. http://dx.doi.org/10.1109/access.2018.2876425.

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28

Zhang, Bo-Wen, Xu-Cheng Yin, and Fang Zhou. "A generic pseudo relevance feedback framework with heterogeneous social information." Information Sciences 367-368 (November 2016): 909–26. http://dx.doi.org/10.1016/j.ins.2016.07.004.

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29

Wasim, Muhammad, Muhammad Nabeel Asim, Muhammad Usman Ghani, Zahoor Ur Rehman, Seungmin Rho, and Irfan Mehmood. "Lexical paraphrasing and pseudo relevance feedback for biomedical document retrieval." Multimedia Tools and Applications 78, no. 21 (2018): 29681–712. http://dx.doi.org/10.1007/s11042-018-6060-z.

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30

Jalali, Vahid, and Mohammad Reza Matash Borujerdi. "Information retrieval with concept-based pseudo-relevance feedback in MEDLINE." Knowledge and Information Systems 29, no. 1 (2010): 237–48. http://dx.doi.org/10.1007/s10115-010-0327-7.

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31

Bashir, Shariq. "Improving retrievability with improved cluster-based pseudo-relevance feedback selection." Expert Systems with Applications 39, no. 8 (2012): 7495–502. http://dx.doi.org/10.1016/j.eswa.2012.01.041.

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32

Jabri, Siham, Azzeddine Dahbi, and Taoufiq Gadi. "A Graph-based approach for text query expansion using pseudo relevance feedback and association rules mining." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 5016. http://dx.doi.org/10.11591/ijece.v9i6.pp5016-5023.

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Pseudo-relevance feedback is a query expansion approach whose terms are selected from a set of top ranked retrieved documents in response to the original query. However, the selected terms will not be related to the query if the top retrieved documents are irrelevant. As a result, retrieval performance for the expanded query is not improved, compared to the original one. This paper suggests the use of documents selected using Pseudo Relevance Feedback for generating association rules. Thus, an algorithm based on dominance relations is applied. Then the strong correlations between query and oth

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33

Siham, Jabri, Dahbi Azzeddine, and Gadi Taoufiq. "A graph-based approach for text query expansion using pseudo relevance feedback and association rules mining." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 5016–23. https://doi.org/10.11591/ijece.v9i6.pp5016-5023.

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Abstract:

Pseudo-relevance feedback is a query expansion approach whose terms are selected from a set of top ranked retrieved documents in response to the original query. However, the selected terms will not be related to the query if the top retrieved documents are irrelevant. As a result, retrieval performance for the expanded query is not improved, compared to the original one. This paper suggests the use of documents selected using Pseudo Relevance Feedback for generating association rules. Thus, an algorithm based on dominance relations is applied. Then the strong correlations between query and oth

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34

Wang, Junmei, Min Pan, Tingting He, Xiang Huang, Xueyan Wang, and Xinhui Tu. "A Pseudo-relevance feedback framework combining relevance matching and semantic matching for information retrieval." Information Processing & Management 57, no. 6 (2020): 102342. http://dx.doi.org/10.1016/j.ipm.2020.102342.

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35

Bashir, Shariq. "An Improved Retrievability-Based Cluster-Resampling Approach for Pseudo Relevance Feedback." Computers 5, no. 4 (2016): 29. http://dx.doi.org/10.3390/computers5040029.

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36

Reuben, Maor, Aviad Elyashar, and Rami Puzis. "Iterative query selection for opaque search engines with pseudo relevance feedback." Expert Systems with Applications 201 (September 2022): 117027. http://dx.doi.org/10.1016/j.eswa.2022.117027.

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37

Valcarce, Daniel, Javier Parapar, and Álvaro Barreiro. "Document-based and term-based linear methods for pseudo-relevance feedback." ACM SIGAPP Applied Computing Review 18, no. 4 (2019): 5–17. http://dx.doi.org/10.1145/3307624.3307626.

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38

Wasim, Muhammad, Muhammad Usman Ghani Khan, and Waqar Mahmood. "Enhanced Biomedical Retrieval Using Discriminative Term Selection for Pseudo Relevance Feedback." Journal of Medical Imaging and Health Informatics 8, no. 5 (2018): 1000–1008. http://dx.doi.org/10.1166/jmihi.2018.2386.

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39

Lin, Wei-Chao, Zong-Yao Chen, Shih-Wen Ke, Chih-Fong Tsai, and Wei-Yang Lin. "The effect of low-level image features on pseudo relevance feedback." Neurocomputing 166 (October 2015): 26–37. http://dx.doi.org/10.1016/j.neucom.2015.04.037.

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40

Khennak, Ilyes, and Habiba Drias. "Strength Pareto fitness assignment for pseudo-relevance feedback: application to MEDLINE." Frontiers of Computer Science 12, no. 1 (2017): 163–76. http://dx.doi.org/10.1007/s11704-016-5560-0.

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41

Ko, Youngjoong, Hongkuk An, and Jungyun Seo. "Pseudo-relevance feedback and statistical query expansion for web snippet generation." Information Processing Letters 109, no. 1 (2008): 18–22. http://dx.doi.org/10.1016/j.ipl.2008.08.004.

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42

Ye, Zheng, Jimmy Xiangji Huang, and Hongfei Lin. "Finding a good query-related topic for boosting pseudo-relevance feedback." Journal of the American Society for Information Science and Technology 62, no. 4 (2011): 748–60. http://dx.doi.org/10.1002/asi.21501.

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43

Ye, Zheng, and Jimmy Xiangji Huang. "A learning to rank approach for quality-aware pseudo-relevance feedback." Journal of the Association for Information Science and Technology 67, no. 4 (2015): 942–59. http://dx.doi.org/10.1002/asi.23430.

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44

Pan, Min, Jimmy Xiangji Huang, Tingting He, Zhiming Mao, Zhiwei Ying, and Xinhui Tu. "A simple kernel co‐occurrence‐based enhancement for pseudo‐relevance feedback." Journal of the Association for Information Science and Technology 71, no. 3 (2019): 264–81. http://dx.doi.org/10.1002/asi.24241.

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45

Lehtokangas, Raija, Heikki Keskustalo, and Kalervo Järvelin. "Experiments with dictionary-based CLIR using graded relevance assessments: Improving effectiveness by pseudo-relevance feedback." Information Retrieval 9, no. 4 (2006): 421–33. http://dx.doi.org/10.1007/s10791-006-6389-1.

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46

Yoo, Sooyoung, and Jinwook Choi. "Evaluation of Term Ranking Algorithms for Pseudo-Relevance Feedback in MEDLINE Retrieval." Healthcare Informatics Research 17, no. 2 (2011): 120. http://dx.doi.org/10.4258/hir.2011.17.2.120.

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47

Yimamuaishan Abudoulikemu, Rui Jiang, TingTing He, and DAWEL Abilhaye. "Kazakh Concept Query Comparison Based on Pseudo Relevance Feedback Query Expansion Algorithm." International Journal of Advancements in Computing Technology 5, no. 5 (2013): 748–55. http://dx.doi.org/10.4156/ijact.vol5.issue5.90.

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48

Khennak, Ilyes, and Habiba Drias. "Proximity-Based Good Turing Discounting and Kernel Functions for Pseudo-Relevance Feedback." International Journal of Information Retrieval Research 7, no. 3 (2017): 1–21. http://dx.doi.org/10.4018/ijirr.2017070101.

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During the last few years, it has become abundantly clear that the technological advances in information technology have led to the dramatic proliferation of information on the web and this, in turn, has led to the appearance of new words in the Internet. Due to the difficulty of reaching the meanings of these new terms, which play an essential role in retrieving the desired information, it becomes necessary to give more importance to the sites and topics where these new words appear, or rather, to give value to the words that occur frequently with them. For this purpose, in this paper, the au

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49

Dang, Edward Kai Fung, Robert Wing Pong Luk, and Qing Li. "A Study of Word Bigrams for Pseudo-relevance Feedback in Information Retrieval." JUCS - Journal of Universal Computer Science 30, no. 11 (2024): 1511–28. http://dx.doi.org/10.3897/jucs.112725.

Full text

Abstract:

Traditional information retrieval models mostly adopt a term independence assumption and are based on single terms or unigrams. Past efforts have attempted to go beyond this assumption, such as by using contiguous terms (i.e. word n-grams) or terms appearing in proximity. One such approach employs pseudo-relevance feedback (PRF) in an extended BM25 model, with an expanded query containing bigrams and proximity word pairs besides unigrams. However, the benefit of this approach over the traditional unigram PRF remains inconclusive. We speculate the uncertain effectiveness of bigram PRF in this p

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50

Khan, Tajmir, Umer Rashid, Abdur Rehman Khan, Naveed Ahmad, and Mohammed Ali Alshara. "End-to-end vertical web search pseudo relevance feedback queries recommendation software." SoftwareX 27 (September 2024): 101872. http://dx.doi.org/10.1016/j.softx.2024.101872.

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