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Journal articles on the topic 'Latent indexing'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

Wang, Quan, Jun Xu, Hang Li, and Nick Craswell. "Regularized Latent Semantic Indexing." ACM Transactions on Information Systems 31, no. 1 (2013): 1–44. http://dx.doi.org/10.1145/2414782.2414787.

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2

Hofmann, Thomas. "Probabilistic Latent Semantic Indexing." ACM SIGIR Forum 51, no. 2 (2017): 211–18. http://dx.doi.org/10.1145/3130348.3130370.

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3

Deerwester, Scott, Susan T. Dumais, George W. Furnas, Thomas K. Landauer, and Richard Harshman. "Indexing by latent semantic analysis." Journal of the American Society for Information Science 41, no. 6 (1990): 391–407. http://dx.doi.org/10.1002/(sici)1097-4571(199009)41:6<391::aid-asi1>3.0.co;2-9.

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4

Thorleuchter, Dirk, and Dirk Van den Poel. "Technology classification with latent semantic indexing." Expert Systems with Applications 40, no. 5 (2013): 1786–95. http://dx.doi.org/10.1016/j.eswa.2012.09.023.

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5

Papadimitriou, Christos H., Prabhakar Raghavan, Hisao Tamaki, and Santosh Vempala. "Latent Semantic Indexing: A Probabilistic Analysis." Journal of Computer and System Sciences 61, no. 2 (2000): 217–35. http://dx.doi.org/10.1006/jcss.2000.1711.

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6

Ayadi, Rami, Mohsen Maraoui, and Mounir Zrigui. "Latent Topic Model for Indexing Arabic Documents." International Journal of Information Retrieval Research 4, no. 1 (2014): 29–45. http://dx.doi.org/10.4018/ijirr.2014010102.

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In this paper, the authors present latent topic model to index and represent the Arabic text documents reflecting more semantics. Text representation in a language with high inflectional morphology such as Arabic is not a trivial task and requires some special treatments. The authors describe our approach for analyzing and preprocessing Arabic text then we describe the stemming process. Finally, the latent model (LDA) is adapted to extract Arabic latent topics, the authors extracted significant topics of all texts, each theme is described by a particular distribution of descriptors then each t

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7

Ayadi, Rami, Mohsen Maraoui, and Mounir Zrigui. "Latent Topic Model for Indexing Arabic Documents." International Journal of Information Retrieval Research 4, no. 2 (2014): 57–72. http://dx.doi.org/10.4018/ijirr.2014040104.

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In this paper, the authors present latent topic model to index and represent the Arabic text documents reflecting more semantics. Text representation in a language with high inflectional morphology such as Arabic is not a trivial task and requires some special treatments. The authors describe their approach for analyzing and preprocessing Arabic text then they describe the stemming process. Finally, the latent model (LDA) is adapted to extract Arabic latent topics, the authors extracted significant topics of all texts, each theme is described by a particular distribution of descriptors then ea

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8

SivaKumar, A. P., P. Premchand, and A. Govardhan. "Indian Languages IR using Latent Semantic Indexing." International Journal of Computer Science and Information Technology 3, no. 4 (2011): 245–53. http://dx.doi.org/10.5121/ijcsit.2011.3419.

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9

Rahim, Robbi, Nuning Kurniasih, Muhammad Dedi Irawan, et al. "Latent Semantic Indexing for Indonesian Text Similarity." International Journal of Engineering & Technology 7, no. 2.3 (2018): 73. http://dx.doi.org/10.14419/ijet.v7i2.3.12619.

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Document is a written letter that can be used as evidence of information. Plagiarism is a deliberate or unintentional act of obtaining or attempting to obtain credit or value for a scientific work, citing some or all of the scientific work of another party acknowledged as a scientific work without stating the source properly and adequately. Latent Semantic Indexing method serves to find text that has the same text against from a document. The algorithm used is TF/IDF Algorithm that is the result of multiplication of TF value with IDF for a term in document while Vector Space Model (VSM) is met

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10

Vecharynski, Eugene, and Yousef Saad. "Fast Updating Algorithms for Latent Semantic Indexing." SIAM Journal on Matrix Analysis and Applications 35, no. 3 (2014): 1105–31. http://dx.doi.org/10.1137/130940414.

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11

Gao, Jing, and Jun Zhang. "Clustered SVD strategies in latent semantic indexing." Information Processing & Management 41, no. 5 (2005): 1051–63. http://dx.doi.org/10.1016/j.ipm.2004.10.005.

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12

Foltz, P. W. "Using latent semantic indexing for information filtering." ACM SIGOIS Bulletin 11, no. 2-3 (1990): 40–47. http://dx.doi.org/10.1145/91478.91486.

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13

Zha, Hongyuan, and Horst D. Simon. "On Updating Problems in Latent Semantic Indexing." SIAM Journal on Scientific Computing 21, no. 2 (1999): 782–91. http://dx.doi.org/10.1137/s1064827597329266.

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14

Thorleuchter, Dirk, and Dirk Van den Poel. "Improved multilevel security with latent semantic indexing." Expert Systems with Applications 39, no. 18 (2012): 13462–71. http://dx.doi.org/10.1016/j.eswa.2012.06.002.

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15

Ding, Chris H. Q. "A probabilistic model for Latent Semantic Indexing." Journal of the American Society for Information Science and Technology 56, no. 6 (2005): 597–608. http://dx.doi.org/10.1002/asi.20148.

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16

Li, Min Song. "A Method Based on Support Vector Machine for Feature Selection of Latent Semantic Features." Advanced Materials Research 181-182 (January 2011): 830–35. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.830.

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Latent Semantic Indexing(LSI) is an effective feature extraction method which can capture the underlying latent semantic structure between words in documents. However, it is probably not the most appropriate for text categorization to use the method to select feature subspace, since the method orders extracted features according to their variance,not the classification power. We proposed a method based on support vector machine to extract features and select a Latent Semantic Indexing that be suited for classification. Experimental results indicate that the method improves classification perfo

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17

Susanto, Gaguk, and Hari Lugis Purwanto. "Information Retrieval Menggunakan Latent Semantic Indexing Pada Ebook." SMATIKA JURNAL 8, no. 02 (2018): 74–79. http://dx.doi.org/10.32664/smatika.v8i02.204.

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Ebook merupakan buku elektronik sebagai pengganti buku kertas yang dapat dibuka dalam perangkat elektronik (smartphone, laptop atau PC). Bagi guru/pengajar maupun siswa/mahasiswa tentunya memiliki koleksi ebook yang banyak dalam komputer dan lebih cenderung hanya tersimpan dalam hardisk dan jarang sekali dibuka padahal isi dalam ebook tersebut mengandung bermacam-macam ilmu pengetahun. Ilmu pengetahuan dari ebook tersebut tentunya dapat dimanfaat untuk merubah komputer menjadi asisten cerdas yang sangat bermanfaat bagi pemiliknya. Asisten cerdas akan memberikan referensi dengan cepat mengenai

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18

Guha, Radha. "EXPLORING INFORMATION RETRIEVAL BY LATENT SEMANTIC INDEXING AND LATENT DIRICHLET ALLOCATION TECHNIQUES." International Research Journal of Computer Science 07, no. 05 (2020): 54–65. http://dx.doi.org/10.26562/irjcs.2020.v0705.001.

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19

Radha, Guha. "EXPLORING INFORMATION RETRIEVAL BY LATENT SEMANTIC INDEXING AND LATENT DIRICHLET ALLOCATION TECHNIQUES." International Research Journal of Computer Science VII, no. V (2020): 54–65. https://doi.org/10.26562/irjcs.2020.v0705.001.

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Today we are living in modern Internet era. We can get all our information from the internet anytime and from anywhere using a desktop PC or a smart phone. However, the underlying technology for relevant information retrieval from the internet is not so trivial, as internet is a huge repository of all different kinds of information. Moreover, data collection in the internet is growing exponentially and it is termed now a day as Big Data era. Retrieving the relevant information from the internet in the Big Data era is same as finding a needle in the haystack. This paper explores information ret

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20

Phadnis, Neelam, and Jayant Gadge. "Framework for Document Retrieval using Latent Semantic Indexing." International Journal of Computer Applications 94, no. 14 (2014): 37–41. http://dx.doi.org/10.5120/16414-6065.

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21

Jiang, Jingqian, Michael W. Berry, June M. Donato, George Ostrouchov, and Nancy W. Grady. "Mining consumer product data via latent semantic indexing☆." Intelligent Data Analysis 3, no. 5 (1999): 377–98. http://dx.doi.org/10.3233/ida-1999-3505.

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22

Parajuli, Srijana, and Subarna Shakya. "Malware Detection and Classification Using Latent Semantic Indexing." Journal of Advanced College of Engineering and Management 4 (December 31, 2018): 153–61. http://dx.doi.org/10.3126/jacem.v4i0.23205.

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The increasing popularity of smart phones has led to the dramatic growth in mobile malware especially in Android platform. Many aspects of android permission has been studied for malware detection but sufficient attention has not been given to intent. This research work proposes using Latent Semantic Indexing for malware detection and classification with permissions and intents based features. This method analyses the Manifest file of an android application by understanding the risk level of permission and intents and assigning weight score based on their sensitivity. In an experiment conducte

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23

Jiang, J. "Mining consumer product data via latent semantic indexing." Intelligent Data Analysis 3, no. 5 (1999): 377–98. http://dx.doi.org/10.1016/s1088-467x(99)00029-3.

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24

Kim, Jeong-Joon, Yong-Soo Lee, Jin-Yong Moon, and Jeong-Min Park. "Document Classification Method based on Latent Semantic Indexing." International Journal of Grid and Distributed Computing 11, no. 4 (2018): 97–112. http://dx.doi.org/10.14257/ijgdc.2018.11.4.09.

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25

Letsche, Todd A., and Michael W. Berry. "Large-scale information retrieval with latent semantic indexing." Information Sciences 100, no. 1-4 (1997): 105–37. http://dx.doi.org/10.1016/s0020-0255(97)00044-3.

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26

Atreya, Avinash, and Charles Elkan. "Latent semantic indexing (LSI) fails for TREC collections." ACM SIGKDD Explorations Newsletter 12, no. 2 (2011): 5–10. http://dx.doi.org/10.1145/1964897.1964900.

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27

Ai, Dongmei, Yuchao Zheng, and Dezheng Zhang. "Automatic text summarization based on latent semantic indexing." Artificial Life and Robotics 15, no. 1 (2010): 25–29. http://dx.doi.org/10.1007/s10015-010-0759-x.

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28

Thorleuchter, Dirk, and Dirk Van den Poel. "Quantitative cross impact analysis with latent semantic indexing." Expert Systems with Applications 41, no. 2 (2014): 406–11. http://dx.doi.org/10.1016/j.eswa.2013.07.066.

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29

Efron, Miles. "Eigenvalue-based model selection during latent semantic indexing." Journal of the American Society for Information Science and Technology 56, no. 9 (2005): 969–88. http://dx.doi.org/10.1002/asi.20188.

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30

Gordon, Michael D., and Susan Dumais. "Using latent semantic indexing for literature based discovery." Journal of the American Society for Information Science 49, no. 8 (1998): 674–85. http://dx.doi.org/10.1002/(sici)1097-4571(199806)49:8<674::aid-asi2>3.0.co;2-t.

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31

Berry, Michael W., and Paul G. Young. "Using latent semantic indexing for multilanguage information retrieval." Computers and the Humanities 29, no. 6 (1995): 413–29. http://dx.doi.org/10.1007/bf01829874.

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32

Xu, Yanyan, Dengfeng Ke, and Kaile Su. "Contextualized Latent Semantic Indexing: A New Approach to Automated Chinese Essay Scoring." Journal of Intelligent Systems 26, no. 2 (2017): 263–85. http://dx.doi.org/10.1515/jisys-2015-0048.

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AbstractThe writing part in Chinese language tests is badly in need of a mature automated essay scoring system. In this paper, we propose a new approach applied to automated Chinese essay scoring (ACES), called contextualized latent semantic indexing (CLSI), of which Genuine CLSI and Modified CLSI are two versions. The n-gram language model and the weighted finite-state transducer (WFST), two critical components, are used to extract context information in our ACES system. Not only does CLSI improve conventional latent semantic indexing (LSI), but bridges the gap between latent semantics and th

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33

Srinivas, S., and Ch AswaniKumar. "Optimising the Heuristics in Latent Semantic Indexing for Effective Information Retrieval." Journal of Information & Knowledge Management 05, no. 02 (2006): 97–105. http://dx.doi.org/10.1142/s0219649206001359.

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Latent Semantic Indexing (LSI) is a famous Information Retrieval (IR) technique that tries to overcome the problems of lexical matching using conceptual indexing. LSI is a variant of vector space model and proved to be 30% more effective. Many studies have reported that good retrieval performance is related to the use of various retrieval heuristics. In this paper, we focus on optimising two LSI retrieval heuristics: term weighting and rank approximation. The results obtained demonstrate that the LSI performance improves significantly with the combination of optimised term weighting and rank a

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34

Hájek, Petr, Michal Střižík, Pavel Praks, and Petr Kadeřábek. "Possibilities of Financial Crises Forecasting with Latent Semantic Indexing." Politická ekonomie 57, no. 6 (2009): 754–68. http://dx.doi.org/10.18267/j.polek.708.

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35

Gu, Shan, Jianjiang Feng, Jiwen Lu, and Jie Zhou. "Latent Fingerprint Indexing: Robust Representation and Adaptive Candidate List." IEEE Transactions on Information Forensics and Security 17 (2022): 908–23. http://dx.doi.org/10.1109/tifs.2022.3154296.

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36

LI, Yuan-yuan. "Text term weighting approach based on latent semantic indexing." Journal of Computer Applications 28, no. 6 (2008): 1460–62. http://dx.doi.org/10.3724/sp.j.1087.2008.01460.

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37

Blynova, N. "Latent semantic indexing (LSI) and its impact on copywriting." Communications and Communicative Technologies, no. 19 (May 5, 2019): 4–12. http://dx.doi.org/10.15421/291901.

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Latent semantic indexing (LSI) is becoming more and more popular in copywriting, gradually replacing texts written on the principles of SEO. LSI was called in the 2010s, when popular search engines switched to a qualitatively new way of ranking materials and sites. The difference between SEO and LSI ways of creation lies in the fact that search engines rank SEO materials by keywords, while LSI are ranked how fully the topic is covered and how useful the article will be to the reader. Consequently, in addition to keywords and phrases, the associative core is involved here. Materials written for

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38

Klie, Sebastian, Lennart Martens, Juan Antonio Vizcaíno, et al. "Analyzing Large-Scale Proteomics Projects with Latent Semantic Indexing." Journal of Proteome Research 7, no. 1 (2008): 182–91. http://dx.doi.org/10.1021/pr070461k.

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39

Hull, Richard D., Suresh B. Singh, Robert B. Nachbar, Robert P. Sheridan, Simon K. Kearsley, and Eugene M. Fluder. "Latent Semantic Structure Indexing (LaSSI) for Defining Chemical Similarity." Journal of Medicinal Chemistry 44, no. 8 (2001): 1177–84. http://dx.doi.org/10.1021/jm000393c.

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40

Freeman, Jared T., Bryan B. Thompson, and Marvin S. Cohen. "Modeling and Diagnosing Domain Knowledge Using Latent Semantic Indexing." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 43, no. 3 (1999): 233–36. http://dx.doi.org/10.1177/154193129904300322.

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41

Schuster, Christof, and David A. Smith. "Indexing systematic rater agreement with a latent-class model." Psychological Methods 7, no. 3 (2002): 384–95. http://dx.doi.org/10.1037/1082-989x.7.3.384.

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42

Moniharapon, Glenzkovic Samuel, Dedi Iskandar Inan, Ratna Juita, and Victor Arie Lambadya Sirait. "Analysis Sentiment & Topic Modelling using Latent Semantic Indexing." G-Tech: Jurnal Teknologi Terapan 8, no. 4 (2024): 2720–32. http://dx.doi.org/10.70609/gtech.v8i4.5434.

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The proliferation of digital news platforms has transformed information consumption, making user experience (UX) a key success factor for Detik.com, a leading news application in Indonesia. This research aims to explore the relationship between UX and news through sentiment analysis and topic modelling. The methods include web scraping for data collection, descriptive analysis for sentiment, and Latent Semantic Indexing (LSI) for topic modelling. Out of 14,320 reviews analyzed, 45.6% were positive, focusing on the speed of updates and news quality. Conversely, 41.18% of reviews were negative,

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43

Al-Zoghby, Aya M., and Khaled Shaalan. "Ontological Optimization for Latent Semantic Indexing of Arabic Corpus." Procedia Computer Science 142 (2018): 206–13. http://dx.doi.org/10.1016/j.procs.2018.10.477.

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44

Freeman, Jared T., Bryan T. Thompson, and Marvin S. Cohen. "Modeling and Diagnosing Domain Knowledge Using Latent Semantic Indexing." Interactive Learning Environments 8, no. 3 (2000): 187–209. http://dx.doi.org/10.1076/1049-4820(200012)8:3;1-d;ft187.

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45

González, Javier, Alberto Muñoz, and Gabriel Martos. "Asymmetric latent semantic indexing for gene expression experiments visualization." Journal of Bioinformatics and Computational Biology 14, no. 04 (2016): 1650023. http://dx.doi.org/10.1142/s0219720016500232.

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We propose a new method to visualize gene expression experiments inspired by the latent semantic indexing technique originally proposed in the textual analysis context. By using the correspondence word-gene document-experiment, we define an asymmetric similarity measure of association for genes that accounts for potential hierarchies in the data, the key to obtain meaningful gene mappings. We use the polar decomposition to obtain the sources of asymmetry of the similarity matrix, which are later combined with previous knowledge. Genetic classes of genes are identified by means of a mixture mod

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46

Kouassi, Eli Koffi, Toshiyuki Amagasa, and Hiroyuki Kitagawa. "Efficient Probabilistic Latent Semantic Indexing using Graphics Processing Unit." Procedia Computer Science 4 (2011): 382–91. http://dx.doi.org/10.1016/j.procs.2011.04.040.

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47

Kontostathis, April, and William M. Pottenger. "A framework for understanding Latent Semantic Indexing (LSI) performance." Information Processing & Management 42, no. 1 (2006): 56–73. http://dx.doi.org/10.1016/j.ipm.2004.11.007.

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48

Bae, Soo Hyun, and Biing-Hwang Juang. "IPSILON: Incremental Parsing for Semantic Indexing of Latent Concepts." IEEE Transactions on Image Processing 19, no. 7 (2010): 1933–47. http://dx.doi.org/10.1109/tip.2010.2045019.

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49

Jaber, T., A. Amira, and P. Milligan. "Enhanced approach for latent semantic indexing using wavelet transform." IET Image Processing 6, no. 9 (2012): 1236–45. http://dx.doi.org/10.1049/iet-ipr.2011.0498.

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50

Homayouni, R., K. Heinrich, L. Wei, and M. W. Berry. "Gene clustering by Latent Semantic Indexing of MEDLINE abstracts." Bioinformatics 21, no. 1 (2004): 104–15. http://dx.doi.org/10.1093/bioinformatics/bth464.

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