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Journal articles on the topic 'Pseudo relevance feedback'

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Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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

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2

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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3

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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4

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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5

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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6

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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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.2017.10009576.

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8

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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9

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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10

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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11

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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12

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 (January 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 karena hasil dari Word Embedding dapat memberikan kata-kata yang sering muncul bersama dengan kata-kata dalam query. Hasil dari word embedding dipakai sebagai masukan pada pseudo relevance feedback untuk diperkaya berdasarkan dokumen jawaban yang telah ada. Metode QE diterapkan dan diuji coba pada aplikasi chatbot. Hasil dari uji coba metode QE yang diterapkan pada chatbot didapatkan nilai recall, precision, dan F-measure masing-masing 100%; 70% dan 82,35 %. Hasil tersebut meningkat 1,49% daripada chatbot tanpa menggunakan QE yang pernah dilakukan sebelumnya yang hanya meraih akurasi sebesar 68,51%. Berdasarkan hasil pengukuran tersebut, QE menggunakan word embedding dan pseudo relevance feedback pada chatbot dapat mengatasi query masukan dari pengguna yang ambigu dan alami, sehingga dapat memberikan jawaban yang relevan kepada pengguna. Keywords are the most important words and phrases used to obtain relevant information on content. Although users make use of natural languages, keywords are processed as queries by the system due to its inability to process. The language directly entered by the user is known as query expansion (QE). The proposed QE in this research uses word embedding owing to its ability to provide words that often appear along with those in the query. The results are used as inputs to the pseudo relevance feedback to be enriched based on the existing documents. This method is also applied to the chatbot application and precision, and F-measure values of the results obtained were 100%, 70%, 82.35% respectively. The results are 1.49% better than chatbot without using QE with 68.51% accuracy. Based on the results of these measurements, QE using word embedding and pseudo which gave relevance feedback in chatbots can resolve ambiguous and natural user’s input queries thereby enabling the system retrieve relevant answers.

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13

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 (March 31, 2012): 533–40. http://dx.doi.org/10.6109/jkiice.2012.16.3.533.

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14

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 (May 15, 2015): 1–33. http://dx.doi.org/10.1145/2744199.

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15

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 (November 15, 2016): 124–31. http://dx.doi.org/10.1002/tee.22352.

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16

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 (July 9, 2015): 246–60. http://dx.doi.org/10.1177/0165551515594722.

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17

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 (September 2006): 1151–62. http://dx.doi.org/10.1016/j.ipm.2006.01.003.

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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 (June 25, 2016): 11889–916. http://dx.doi.org/10.1007/s11042-016-3678-6.

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19

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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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 (August 9, 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 distribution in the set of top pseudo-relevant documents along with their similarity to the original query terms. Experiments are conducted on the standard Arabic TREC 2001/2002 collection using three neural WE models. The obtained results show that our PRF extensions significantly outperform their baseline PRF models. Moreover, they enhanced the baseline IR model by 22% and 68% for the mean average precision (MAP) and the robustness index (RI), respectively.

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21

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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22

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 (June 4, 2018): 29681–712. http://dx.doi.org/10.1007/s11042-018-6060-z.

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23

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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24

Jalali, Vahid, and Mohammad Reza Matash Borujerdi. "Information retrieval with concept-based pseudo-relevance feedback in MEDLINE." Knowledge and Information Systems 29, no. 1 (July 21, 2010): 237–48. http://dx.doi.org/10.1007/s10115-010-0327-7.

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25

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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26

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

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27

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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28

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 (November 2020): 102342. http://dx.doi.org/10.1016/j.ipm.2020.102342.

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29

Bashir, Shariq. "An Improved Retrievability-Based Cluster-Resampling Approach for Pseudo Relevance Feedback." Computers 5, no. 4 (November 15, 2016): 29. http://dx.doi.org/10.3390/computers5040029.

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30

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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31

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 (June 1, 2018): 1000–1008. http://dx.doi.org/10.1166/jmihi.2018.2386.

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32

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 (February 23, 2011): 748–60. http://dx.doi.org/10.1002/asi.21501.

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33

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 (May 13, 2015): 942–59. http://dx.doi.org/10.1002/asi.23430.

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34

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 (May 13, 2019): 264–81. http://dx.doi.org/10.1002/asi.24241.

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35

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 (January 15, 2019): 5–17. http://dx.doi.org/10.1145/3307624.3307626.

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36

Khennak, Ilyes, and Habiba Drias. "Strength Pareto fitness assignment for pseudo-relevance feedback: application to MEDLINE." Frontiers of Computer Science 12, no. 1 (October 18, 2017): 163–76. http://dx.doi.org/10.1007/s11704-016-5560-0.

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37

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

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38

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 (September 2006): 421–33. http://dx.doi.org/10.1007/s10791-006-6389-1.

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39

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 (December 1, 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 other terms are detected, and an oriented and weighted graph called Pseudo-Graph Feedback is constructed. This graph serves for expanding original queries by terms related semantically and selected by the user. The results of the experiments on Text Retrieval Conference (TREC) collection are very significant, and best results are achieved by the proposed approach compared to both the baseline system and an existing technique.

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40

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 (July 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 authors propose a new robust correlation measure that assesses the relatedness of words for pseudo-relevance feedback. It is based on the co-occurrence and closeness of terms, and aims to select the appropriate words that best capture the user information need. Extensive experiments have been conducted on the OHSUMED test collection and the results show that the proposed approach achieves a considerable performance improvement over the baseline.

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41

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 (March 15, 2013): 748–55. http://dx.doi.org/10.4156/ijact.vol5.issue5.90.

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42

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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43

Singh, Jagendra, Mukesh Prasad, Om Kumar Prasad, Er Meng Joo, Amit Kumar Saxena, and Chin-Teng Lin. "A Novel Fuzzy Logic Model for Pseudo-Relevance Feedback-Based Query Expansion." International Journal of Fuzzy Systems 18, no. 6 (October 4, 2016): 980–89. http://dx.doi.org/10.1007/s40815-016-0254-1.

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44

Lee, Kyung Soon, and W. Bruce Croft. "A deterministic resampling method using overlapping document clusters for pseudo-relevance feedback." Information Processing & Management 49, no. 4 (July 2013): 792–806. http://dx.doi.org/10.1016/j.ipm.2013.01.001.

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45

Wang, Le, Ze Luo, Canjia Li, Ben He, Le Sun, Hao Yu, and Yingfei Sun. "An end-to-end pseudo relevance feedback framework for neural document retrieval." Information Processing & Management 57, no. 2 (March 2020): 102182. http://dx.doi.org/10.1016/j.ipm.2019.102182.

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46

Zhong, Min Juan. "An Effective XML Identifying Feedback Documents Method Based on Two-Stage Ranking Model for Pseudo-Relevance Feedback." Advanced Materials Research 791-793 (September 2013): 1593–96. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1593.

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Although pseudo relevant feedback is an effective query expansion method, query drift away from the topic has been occurred frequently. Therefore, the first important problem is how to identify relevant documents in the top retrieved set and form the good feedback source. In this paper, an effective XML identifying feedback documents method is proposed, in which a two-stage ranking model is presented and the relevant XML documents are found. The experiment results show that the proposed method is reasonable and the quality of feedback source is ensured.

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47

Singh, Jagendra, and Aditi Sharan. "Context Window Based Co-occurrence Approach for Improving Feedback Based Query Expansion in Information Retrieval." International Journal of Information Retrieval Research 5, no. 4 (October 2015): 31–45. http://dx.doi.org/10.4018/ijirr.2015100103.

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Pseudo-relevance feedback (PRF) is a type of relevance feedback approach of query expansion that considers the top ranked retrieved documents as relevance feedback. In this paper the authors focus is to capture the limitation of co-occurrence and PRF based query expansion approach and the authors proposed a hybrid method to improve the performance of PRF based query expansion by combining query term co-occurrence and query terms contextual information based on corpus of top retrieved feedback documents in first pass. Firstly, the paper suggests top retrieved feedback documents based query term co-occurrence approach to select an optimal combination of query terms from a pool of terms obtained using PRF based query expansion. Second, contextual window based approach is used to select the query context related terms from top feedback documents. Third, comparisons were made among baseline, co-occurrence and contextual window based approaches using different performance evaluating metrics. The experiments were performed on benchmark data and the results show significant improvement over baseline approach.

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48

HUANG, Ming-Xuan, Xiao-Wei YAN, and Shi-Chao ZHANG. "Query Expansion of Pseudo Relevance Feedback Based on Matrix-Weighted Association Rules Mining." Journal of Software 20, no. 7 (March 10, 2010): 1854–65. http://dx.doi.org/10.3724/sp.j.1001.2009.03368.

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49

Karamti, H., M. Tmar, M. Visani, T. Urruty, and F. Gargouri. "Vector space model adaptation and pseudo relevance feedback for content-based image retrieval." Multimedia Tools and Applications 77, no. 5 (March 15, 2017): 5475–501. http://dx.doi.org/10.1007/s11042-017-4463-x.

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50

CHANG, F. C. "A Relevance Feedback Image Retrieval Scheme Using Multi-Instance and Pseudo Image Concepts." IEICE Transactions on Information and Systems E89-D, no. 5 (May 1, 2006): 1720–31. http://dx.doi.org/10.1093/ietisy/e89-d.5.1720.

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