Relevant bibliographies by topics / Latent semantic analysis (LSA)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Latent semantic analysis (LSA)'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Latent semantic analysis (LSA)"

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Shrabanti, Mandal, and Kumar Singh Girish. "LSA Based Text Summarization." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 2 (2020): 150–56. https://doi.org/10.35940/ijrte.B3288.079220.

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In this study we propose an automatic single document text summarization technique using Latent Semantic Analysis (LSA) and diversity constraint in combination. The proposed technique uses the query based sentence ranking. Here we are not considering the concept of IR (Information Retrieval) so we generate the query by using the TF-IDF(Term Frequency-Inverse Document Frequency). For producing the query vector, we identify the terms having the high IDF. We know that LSA utilizes the vectorial semantics to analyze the relationships between documents in a corpus or between sentences within a docu
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LOUWERSE, MAX, ZHIQIANG CAI, XIANGEN HU, MATTHEW VENTURA, and PATRICK JEUNIAUX. "COGNITIVELY INSPIRED NLP-BASED KNOWLEDGE REPRESENTATIONS: FURTHER EXPLORATIONS OF LATENT SEMANTIC ANALYSIS." International Journal on Artificial Intelligence Tools 15, no. 06 (2006): 1021–39. http://dx.doi.org/10.1142/s0218213006003090.

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Natural-language based knowledge representations borrow their expressiveness from the semantics of language. One such knowledge representation technique is Latent semantic analysis (LSA), a statistical, corpus-based method for representing knowledge. It has been successfully used in a variety of applications including intelligent tutoring systems, essay grading and coherence metrics. The advantage of LSA is that it is efficient in representing world knowledge without the need for manual coding of relations and that it has in fact been considered to simulate aspects of human knowledge represent
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Garbhapu, Vasantha Kumari. "A comparative analysis of Latent Semantic analysis and Latent Dirichlet allocation topic modeling methods using Bible data." Indian Journal of Science and Technology 13, no. 44 (2020): 4474–82. http://dx.doi.org/10.17485/ijst/v13i44.1479.

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Objective: To compare the topic modeling techniques, as no free lunch theorem states that under a uniform distribution over search problems, all machine learning algorithms perform equally. Hence, here, we compare Latent Semantic Analysis (LSA) or Latent Dirichlet Allocation (LDA) to identify better performer for English bible data set which has not been studied yet. Methods: This comparative study divided into three levels: In the first level, bible data was extracted from the sources and preprocessed to remove the words and characters which were not useful to obtain the semantic structures o
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Vasantha, Kumari Garbhapu, and Bodapati Prajna. "A comparative analysis of Latent Semantic analysis and Latent Dirichlet allocation topic modeling methods using Bible data." Indian Journal of Science and Technology 13, no. 44 (2020): 4474–82. https://doi.org/10.17485/IJST/v13i44.1479.

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Abstract <strong>Objective:</strong>&nbsp;To compare the topic modeling techniques, as no free lunch theorem states that under a uniform distribution over search problems, all machine learning algorithms perform equally. Hence, here, we compare Latent Semantic Analysis (LSA) or Latent Dirichlet Allocation (LDA) to identify better performer for English bible data set which has not been studied yet.&nbsp;<strong>Methods:</strong>&nbsp;This comparative study divided into three levels: In the first level, bible data was extracted from the sources and preprocessed to remove the words and characters
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Patil, Priyanka R., and Shital A. Patil. "Similarity Detection Using Latent Semantic Analysis Algorithm." International Journal of Emerging Research in Management and Technology 6, no. 8 (2018): 102. http://dx.doi.org/10.23956/ijermt.v6i8.124.

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Similarity View is an application for visually comparing and exploring multiple models of text and collection of document. Friendbook finds ways of life of clients from client driven sensor information, measures the closeness of ways of life amongst clients, and prescribes companions to clients if their ways of life have high likeness. Roused by demonstrate a clients day by day life as life records, from their ways of life are separated by utilizing the Latent Dirichlet Allocation Algorithm. Manual techniques can't be utilized for checking research papers, as the doled out commentator may have
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Kondeti, Bhuvaneshwari, Jyothirani S. A, and Haragopal V. V. "Keyword Extraction – Comparison of Latent Dirichlet Allocation and Latent Semantic Analysis." European Journal of Mathematics and Statistics 3, no. 3 (2022): 40–47. http://dx.doi.org/10.24018/ejmath.2022.3.3.119.

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The main aim of the present study is to compare the keywords extracted from abstracts and full length text of scientific research papers. In addition to that, here, we compare Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) to identify better performer for keyword extraction. This comparative study is divided into three levels, In the first level, scientific research articles on topics such as Indian Economic growth, GDP, Economic Slowdown etc. were collected and abstracts and full length text was extracted from the sources and pre-processed to remove the words and charact
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Horasan, Fahrettin, Hasan Erbay, Fatih Varçın, and Emre Deniz. "Alternate Low-Rank Matrix Approximation in Latent Semantic Analysis." Scientific Programming 2019 (February 3, 2019): 1–12. http://dx.doi.org/10.1155/2019/1095643.

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The latent semantic analysis (LSA) is a mathematical/statistical way of discovering hidden concepts between terms and documents or within a document collection (i.e., a large corpus of text). Each document of the corpus and terms are expressed as a vector with elements corresponding to these concepts to form a term-document matrix. Then, the LSA uses a low-rank approximation to the term-document matrix in order to remove irrelevant information, to extract more important relations, and to reduce the computational time. The irrelevant information is called as “noise” and does not have a notewort
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Ghanem, Khadoudja. "Local and Global Latent Semantic Analysis for Text Categorization." International Journal of Information Retrieval Research 4, no. 3 (2014): 1–13. http://dx.doi.org/10.4018/ijirr.2014070101.

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In this paper the authors propose a semantic approach to document categorization. The idea is to create for each category a semantic index (representative term vector) by performing a local Latent Semantic Analysis (LSA) followed by a clustering process. A second use of LSA (Global LSA) is adopted on a term-Class matrix in order to retrieve the class which is the most similar to the query (document to classify) in the same way where the LSA is used to retrieve documents which are the most similar to a query in Information Retrieval. The proposed system is evaluated on a popular dataset which i
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Tonta, Yaşar, and Hamid Darvish. "Diffusion of latent semantic analysis as a research tool: A social network analysis approach." Journal of Informetrics 4, no. 2010 (2010): 166–74. https://doi.org/10.1016/j.joi.2009.11.003.

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Latent semantic analysis (LSA) is a relatively new research tool with a wide range of applications in different fields ranging from discourse analysis to cognitive science, from information retrieval to machine learning and so on. In this paper, we chart the develop- ment and diffusion of LSA as a research tool using social network analysis (SNA) approach that reveals the social structure of a discipline in terms of collaboration among scientists. Using Thomson Reuters’ Web of Science (WoS), we identified 65 papers with “latent seman- tic analysis” in their titles and 250 papers in their topic
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Mazis, Panagiotis, and Andrianos Tsekrekos. "Latent semantic analysis of the FOMC statements." Review of Accounting and Finance 16, no. 2 (2017): 179–217. http://dx.doi.org/10.1108/raf-10-2015-0149.

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Purpose The purpose of this paper is to analyze the content of the statements that are released by the Federal Open Market Committee (FOMC) after its meetings, identify the main textual associative patterns in the statements and examine their impact on the US treasury market. Design/methodology/approach Latent semantic analysis (LSA), a language processing technique that allows recognition of the textual associative patterns in documents, is applied to all the statements released by the FOMC between 2003 and 2014, so as to identify the main textual “themes” used by the Committee in its communi
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Dissertations / Theses on the topic "Latent semantic analysis (LSA)"

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Makovoz, Gennadiy. "Latent Semantic Analysis as a Method of Content-Based Image Retrieval in Medical Applications." NSUWorks, 2010. http://nsuworks.nova.edu/gscis_etd/227.

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The research investigated whether a Latent Semantic Analysis (LSA)-based approach to image retrieval can map pixel intensity into a smaller concept space with good accuracy and reasonable computational cost. From a large set of computed tomography (CT) images, a retrieval query found all images for a particular patient based on semantic similarity. The effectiveness of the LSA retrieval was evaluated based on precision, recall, and F-score. This work extended the application of LSA to high-resolution CT radiology images. The images were chosen for their unique characteristics and their importa
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Natividad, Beltrán del Río Gloria Ofelia. "An Analysis of Educational Technology Publications: Who, What and Where in the Last 20 Years." Thesis, University of North Texas, 2016. https://digital.library.unt.edu/ark:/67531/metadc849761/.

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This exploratory and descriptive study examines research articles published in ten of the top journals in the broad area of educational technology during the last 20 years: 1) Educational Technology Research and Development (ETR&D); 2) Instructional Science; 3) Journal of the Learning Sciences; 4) TechTrends; 5) Educational Technology: The Magazine for Managers of Change in Education; 6) Journal of Educational Technology & Society; 7) Computers and Education; 8) British Journal of Educational Technology (BJET); 9) Journal of Educational Computing Research; and 10) Journal of Research on Techno
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Hossain, Muhammad Muazzem. "Investigating the relationship between the business performance management framework and the Malcolm Baldrige National Quality Award framework." Thesis, University of North Texas, 2009. https://digital.library.unt.edu/ark:/67531/metadc11034/.

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The business performance management (BPM) framework helps an organization continuously adjust and successfully execute its strategies. BPM helps increase flexibility by providing managers with an early alert about changes and, as a result, allows faster response to such changes. The Malcolm Baldrige National Quality Award (MBNQA) framework provides a basis for self-assessment and a systems perspective for managing an organization's key processes for achieving business results. The MBNQA framework is a more comprehensive framework and encapsulates the underlying constructs in the BPM framework.
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SANTOS, João Carlos Alves dos. "Avaliação automática de questões discursivas usando LSA." Universidade Federal do Pará, 2016. http://repositorio.ufpa.br/jspui/handle/2011/7485.

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Submitted by camilla martins (camillasmmartins@gmail.com) on 2017-01-27T15:50:37Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Tese_AvaliacaoAutomaticaQuestoes.pdf: 5106074 bytes, checksum: c401d50ce5e666c52948ece7af20b2c3 (MD5)<br>Approved for entry into archive by Edisangela Bastos (edisangela@ufpa.br) on 2017-01-30T13:02:31Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Tese_AvaliacaoAutomaticaQuestoes.pdf: 5106074 bytes, checksum: c401d50ce5e666c52948ece7af20b2c3 (MD5)<br>Made available
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Kučaidze, Artiom. "Tinklalapio navigavimo asociacijų analizės ir prognozavimo modelis." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2009. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2008~D_20090908_201802-74260.

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Darbe, remiantis informacijos paieškos teorija, bandoma sukurti tinklalapio navigavimo asociacijų analizės ir prognozavimo modelį. Šio modelio tikslas – simuliuoti potencialių tinklalapio vartotojų informacijos paieškos kelius turint apibrėžtą informacinį tikslą. Modelis kuriamas apjungiant LSA, SVD algoritmus ir koreliacijos koeficientų skaičiavimus. LSA algoritmas naudojamas kuriant semantines erdves, o koreliacijos koeficientų skaičiavimai naudojami statistikoje. Kartu jie leidžia tinklalapio navigavimo asociacijų analizės ir prognozavimo modeliui analizuoti žodžių semantinį panašumą. Darbo
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Belica, Michal. "Metody sumarizace dokumentů na webu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236386.

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The work deals with automatic summarization of documents in HTML format. As a language of web documents, Czech language has been chosen. The project is focused on algorithms of text summarization. The work also includes document preprocessing for summarization and conversion of text into representation suitable for summarization algorithms. General text mining is also briefly discussed but the project is mainly focused on the automatic document summarization. Two simple summarization algorithms are introduced. Then, the main attention is paid to an advanced algorithm that uses latent semantic
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Ozsoy, Makbule Gulcin. "Text Summarization Using Latent Semantic Analysis." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612988/index.pdf.

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Text summarization solves the problem of presenting the information needed by a user in a compact form. There are different approaches to create well formed summaries in literature. One of the newest methods in text summarization is the Latent Semantic Analysis (LSA) method. In this thesis, different LSA based summarization algorithms are explained and two new LSA based summarization algorithms are proposed. The algorithms are evaluated on Turkish and English documents, and their performances are compared using their ROUGE scores.
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Anaya, Leticia H. "Comparing Latent Dirichlet Allocation and Latent Semantic Analysis as Classifiers." Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc103284/.

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In the Information Age, a proliferation of unstructured text electronic documents exists. Processing these documents by humans is a daunting task as humans have limited cognitive abilities for processing large volumes of documents that can often be extremely lengthy. To address this problem, text data computer algorithms are being developed. Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) are two text data computer algorithms that have received much attention individually in the text data literature for topic extraction studies but not for document classification nor for
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Huang, Fang. "Multi-document summarization with latent semantic analysis." Thesis, University of Sheffield, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.419255.

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Buys, Stephanus. "Log analysis aided by latent semantic mapping." Thesis, Rhodes University, 2013. http://hdl.handle.net/10962/d1002963.

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In an age of zero-day exploits and increased on-line attacks on computing infrastructure, operational security practitioners are becoming increasingly aware of the value of the information captured in log events. Analysis of these events is critical during incident response, forensic investigations related to network breaches, hacking attacks and data leaks. Such analysis has led to the discipline of Security Event Analysis, also known as Log Analysis. There are several challenges when dealing with events, foremost being the increased volumes at which events are often generated and stored. Fur
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Books on the topic "Latent semantic analysis (LSA)"

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Miller, Tristan. Generating coherent extracts of single documents using latent semantic analysis. National Library of Canada, 2003.

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Landauer, Thomas K., Danielle S. McNamara, Simon Dennis, and Walter Kintsch, eds. Handbook of Latent Semantic Analysis. Psychology Press, 2007. http://dx.doi.org/10.4324/9780203936399.

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Landauer, Thomas K. Handbook of Latent Semantic Analysis. Taylor & Francis Group, 2007.

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Landauer, Thomas K. Handbook of Latent Semantic Analysis. Taylor & Francis Group, 2007.

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Landauer, Thomas K. Handbook of Latent Semantic Analysis. Taylor & Francis Group, 2007.

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Landauer, Thomas K. Handbook of Latent Semantic Analysis. Taylor & Francis Group, 2007.

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Landauer, Thomas K. Handbook of Latent Semantic Analysis. Taylor & Francis Group, 2007.

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Landauer, Thomas K. Handbook of Latent Semantic Analysis. Taylor & Francis Group, 2014.

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Landauer, Thomas K. Handbook of Latent Semantic Analysis. Taylor & Francis Group, 2007.

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Howse, Mervin. Ultimate Guide to Latent Semantic Analysis. Lulu Press, Inc., 2015.

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Book chapters on the topic "Latent semantic analysis (LSA)"

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Anandarajan, Murugan, Chelsey Hill, and Thomas Nolan. "Latent Semantic Analysis (LSA) in Python." In Practical Text Analytics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95663-3_14.

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Sidorov, Grigori. "Latent Semantic Analysis (LSA): Reduction of Dimensions." In Syntactic n-grams in Computational Linguistics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14771-6_4.

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Wang, Qian, Zhaohui Peng, Fei Jiang, and Qingzhong Li. "LSA-PTM: A Propagation-Based Topic Model Using Latent Semantic Analysis on Heterogeneous Information Networks." In Web-Age Information Management. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38562-9_2.

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Prema N, I. G. N. A. Agni, Faqih Hamami, and Ekky Novriza Alam. "Topic modeling on Natural Tourism Objects in West Bandung Regency Based on Reviews from Google Maps with the Latent Semantic Analysis (LSA) Method." In Advances in Intelligent Systems Research. Atlantis Press International BV, 2024. http://dx.doi.org/10.2991/978-94-6463-366-5_7.

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Li, Hang. "Latent Semantic Analysis." In Machine Learning Methods. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3917-6_17.

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González, Fabio A., and Juan C. Caicedo. "Quantum Latent Semantic Analysis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23318-0_7.

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Li, Hang. "Probabilistic Latent Semantic Analysis." In Machine Learning Methods. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3917-6_18.

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Anandarajan, Murugan, Chelsey Hill, and Thomas Nolan. "Semantic Space Representation and Latent Semantic Analysis." In Practical Text Analytics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95663-3_6.

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Khokale, Rahul, Nileshsingh V. Thakur, Mahendra Makesar, and Nitin A. Koli. "Information Retrieval Using Latent Semantic Analysis." In Smart Innovation, Systems and Technologies. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0077-0_40.

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Řehůřek, Radim. "Subspace Tracking for Latent Semantic Analysis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20161-5_29.

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Conference papers on the topic "Latent semantic analysis (LSA)"

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Bhardwaj, Shambhu, and J. Ghayathri. "Domain Specific Text Summarization Using Latent Semantic Analysis (LSA) and Performing Visualization Using PylDAVIS." In 2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI). IEEE, 2024. https://doi.org/10.1109/icscai61790.2024.10866589.

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Tiutiunnyk, Hanna. "Semantic Analysis of the Concept of “Aquafood System”." In 8th International Congress "Environment Protection. Energy Saving. Sustainable Environmental Management". Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-esc1gn.

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This article explores the evolving concept of the "aquafood system", a term that encompasses the production, distribution, and consumption of aquatic products while prioritizing sustainability and food security. The paper provides a comprehensive semantic analysis to clarify the role of this term in both Ukrainian and global contexts, emphasizing its relevance amid rising aquaculture activities and the pressing need for food security. Recognizing the diversity and complexity of aquafood systems, the author employs latent semantic analysis (LSA) to dissect its underlying dimensions – ecological
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Chang, Kai-Wei, Wen-tau Yih, and Christopher Meek. "Multi-Relational Latent Semantic Analysis." In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2013. http://dx.doi.org/10.18653/v1/d13-1167.

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Curativo, Ernest Joseph, Neil Christian Sagun, and Angie Ceniza-Canillo. "SOGIE Bill Discourse Analysis Using Latent Dirichlet Allocation, Nonnegative Matrix Factorization, BERTopic, and Latent Semantic Analysis." In 2024 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT). IEEE, 2024. https://doi.org/10.1109/comnetsat63286.2024.10862424.

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Slomovitz, Gabriel. "Latent Semantic Analysis (LSA) for syslog correlation." In 2017 International Conference on Electronics, Communications and Computers (CONIELECOMP). IEEE, 2017. http://dx.doi.org/10.1109/conielecomp.2017.7891819.

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Hebballi, Vinaykumar, and Vidhu Rojit. "Latent Semantic Analysis (LSA) based object recognition and clustering." In 2015 International Conference on Green Computing and Internet of Things (ICGCIoT). IEEE, 2015. http://dx.doi.org/10.1109/icgciot.2015.7380499.

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Sanjifa, Zakky Nilem, Surya Sumpeno, and Yoyon Kusnendar Suprapto. "Community Feedback Analysis Using Latent Semantic Analysis (LSA) To Support Smart Government." In 2019 International Seminar on Intelligent Technology and Its Applications (ISITIA). IEEE, 2019. http://dx.doi.org/10.1109/isitia.2019.8937137.

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Nguyen, Khu P., and Huy Q. Phan. "Feasible settings for the adaptive latent semantic analysis: Hk-LSA model." In 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA). IEEE, 2017. http://dx.doi.org/10.1109/ciapp.2017.8167211.

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Ahmad Niam, Ilham Maulana, Budhi Irawan, Casi Setianingsih, and Bagas Prakoso Putra. "Hate Speech Detection Using Latent Semantic Analysis (LSA) Method Based On Image." In 2018 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC). IEEE, 2018. http://dx.doi.org/10.1109/iccerec.2018.8712111.

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Siti Shalihah, Fadhilah, Prima Dewi Purnamasari, Lea Santiar, and Anak Agung Putri Ratna. "Development of the Oral Examination Assessment System (SIPENILAI) in Japanese Using Latent Semantic Analysis (LSA) Algorithm." In ICCIP 2020: 2020 the 6th International Conference on Communication and Information Processing. ACM, 2020. http://dx.doi.org/10.1145/3442555.3442558.

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Reports on the topic "Latent semantic analysis (LSA)"

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Zelenskyi, Arkadii A. Relevance of research of programs for semantic analysis of texts and review of methods of their realization. [б. в.], 2018. http://dx.doi.org/10.31812/123456789/2884.

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One of the main tasks of applied linguistics is the solution of the problem of high-quality automated processing of natural language. The most popular methods for processing natural-language text responses for the purpose of extraction and representation of semantics should be systems that are based on the efficient combination of linguistic analysis technologies and analysis methods. Among the existing methods for analyzing text data, a valid method is used by the method using a vector model. Another effective and relevant means of extracting semantics from the text and its representation is
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Sparks, Randall, and Rex Hartson. The Software Therapist: Usability Problem Diagnosis Through Latent Semantic Analysis. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada458771.

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Lochbaum, Karen E., and Lynn A. Streeter. Carnegie Hall: An Intelligent Tutor for Command-Reasoning Practice Based on Latent Semantic Analysis. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada406129.

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