Relevant bibliographies by topics / Text processing (Computer science)

Academic literature on the topic 'Text processing (Computer science)'

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Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Text processing (Computer science)"

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Geetha, Dr V., Dr C. K. Gomathy, Mr D. Sri Datta Vallab Yaratha Yagn, and Sai Praneesh. "THE ROLE OF NATURAL LANGUAGE PROCESSING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (November 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27094.

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Natural Language Processing (NLP) is a rapidly evolving field in the intersection of computer science, artificial intelligence, and linguistics. This article provides an overview of NLP, tracing its historical development from early rule-based systems to contemporary deep learning models. Natural Language Processing is a subfield of computer science and artificial intelligence that deals with the interactions between computers and humans using natural language. It focuses on the ability of computers to understand, interpret, and generate human language. Keywords Natural Language Processing, Text Analysis , Text Mining , Speech Recognition.
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HAO, KE, ZHIYUAN GONG, CHENGJIA HUO, and PHILLIP C. Y. SHEU. "SEMANTIC COMPUTING AND COMPUTER SCIENCE." International Journal of Semantic Computing 05, no. 01 (March 2011): 95–120. http://dx.doi.org/10.1142/s1793351x11001183.

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Semantic Computing is an emerging research field that has drawn much attention from both academia and industry. It addresses the derivation and matching of semantics of computational "content" where "content" may be anything including text, multimedia, hardware, network, etc. which can be mapped to many areas in Computer Science that involve analyzing and processing the intentions of humans with computational content. This paper discusses some potential applications of Semantic Computing in Computer Science.
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Gorman, Kyle, and Richard Sproat. "Finite-State Text Processing." Synthesis Lectures on Human Language Technologies 14, no. 2 (May 26, 2021): 1–158. http://dx.doi.org/10.2200/s01086ed1v01y202104hlt050.

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Li, Jia. "Learning vocabulary via computer-assisted scaffolding for text processing." Computer Assisted Language Learning 23, no. 3 (July 2010): 253–75. http://dx.doi.org/10.1080/09588221.2010.483678.

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Yaohan Chu. "Chinese/Kanji Text and Data Processing." Computer 18, no. 1 (January 1985): 10–12. http://dx.doi.org/10.1109/mc.1985.1662677.

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Musthofa, Musthofa. "COMPUTATIONAL LINGUISTICS (Model Baru Kajian Linguistik dalam Perspektif Komputer)." Adabiyyāt: Jurnal Bahasa dan Sastra 9, no. 2 (December 31, 2010): 247. http://dx.doi.org/10.14421/ajbs.2010.09203.

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This paper describes a new discipline in applied linguistics studies, computational linguistics. It’s a new model of applied linguistics which is influenced by computer technology. Computational linguistics is a discipline straddling applied linguistics and computer science that is concerned with the computer processing of natural languages on all levels of linguistic description. Traditionally, computational linguistics was usually performed by computer scientists who had specialized in the application of computers to the processing of a natural language. Computational linguists often work as members of interdisciplinary teams, including linguists (specifically trained in linguistics), language experts (persons with some level of ability in the languages relevant to a given project), and computer scientists. The several areas of computational linguistics study encompasses such practical applications as speech recognition systems, speech synthesis, automated voice response systems, web search engines, text editors, grammar checking, text to speech, corpus linguistics, machine translation, text data mining, and others. This paper presents the definition of computational linguistics, relation between language and computer, and area of computational linguistics studies.
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Lebowitz, Michael. "The use of memory in text processing." Communications of the ACM 31, no. 12 (December 1988): 1483–502. http://dx.doi.org/10.1145/53580.214951.

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GOOSSENS, MICHEL, and ERIC VAN HERWIJNEN. "SCIENTIFIC TEXT PROCESSING." International Journal of Modern Physics C 03, no. 03 (June 1992): 479–546. http://dx.doi.org/10.1142/s0129183192000336.

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Aspects of text processing important for the scientific community are discussed, and an overview of currently available software is presented. Progress on standardization efforts in the area of document exchange (SGML), document formatting (DSSSL), document presentation (SPDL), fonts (ISO 9541) and character codes (Unicode and ISO 10646) is described. An elementary particle naming scheme for use with LATEX and SGML is proposed. LATEX, PostScript, SGML and desk-top publishing allow electronic submission of articles to publishers, and printing on demand. Advantages of standardization are illustrated by the description of a system which can exchange documents between different word processors and automatically extract bibliographic data for a library database.
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Lucas, Christopher, Richard A. Nielsen, Margaret E. Roberts, Brandon M. Stewart, Alex Storer, and Dustin Tingley. "Computer-Assisted Text Analysis for Comparative Politics." Political Analysis 23, no. 2 (2015): 254–77. http://dx.doi.org/10.1093/pan/mpu019.

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Recent advances in research tools for the systematic analysis of textual data are enabling exciting new research throughout the social sciences. For comparative politics, scholars who are often interested in non-English and possibly multilingual textual datasets, these advances may be difficult to access. This article discusses practical issues that arise in the processing, management, translation, and analysis of textual data with a particular focus on how procedures differ across languages. These procedures are combined in two applied examples of automated text analysis using the recently introduced Structural Topic Model. We also show how the model can be used to analyze data that have been translated into a single language via machine translation tools. All the methods we describe here are implemented in open-source software packages available from the authors.
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Thessen, Anne E., Hong Cui, and Dmitry Mozzherin. "Applications of Natural Language Processing in Biodiversity Science." Advances in Bioinformatics 2012 (May 22, 2012): 1–17. http://dx.doi.org/10.1155/2012/391574.

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Centuries of biological knowledge are contained in the massive body of scientific literature, written for human-readability but too big for any one person to consume. Large-scale mining of information from the literature is necessary if biology is to transform into a data-driven science. A computer can handle the volume but cannot make sense of the language. This paper reviews and discusses the use of natural language processing (NLP) and machine-learning algorithms to extract information from systematic literature. NLP algorithms have been used for decades, but require special development for application in the biological realm due to the special nature of the language. Many tools exist for biological information extraction (cellular processes, taxonomic names, and morphological characters), but none have been applied life wide and most still require testing and development. Progress has been made in developing algorithms for automated annotation of taxonomic text, identification of taxonomic names in text, and extraction of morphological character information from taxonomic descriptions. This manuscript will briefly discuss the key steps in applying information extraction tools to enhance biodiversity science.
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Dissertations / Theses on the topic "Text processing (Computer science)"

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Nyns, Roland. "Text grammar and text processing: a cognitivist approach." Doctoral thesis, Universite Libre de Bruxelles, 1989. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/213285.

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Zaghloul, Waleed A. Lee Sang M. "Text mining using neural networks." Lincoln, Neb. : University of Nebraska-Lincoln, 2005. http://0-www.unl.edu.library.unl.edu/libr/Dissertations/2005/Zaghloul.pdf.

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Thesis (Ph.D.)--University of Nebraska-Lincoln, 2005.
Title from title screen (sites viewed on Oct. 18, 2005). PDF text: 100 p. : col. ill. Includes bibliographical references (p. 95-100 of dissertation).
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Tumu, Sudheer. "An Investigative and Goal driven Workbench for Text Extraction and Image Processing." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1376930066.

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McCaffrey, Corey (Corey Stanley Gordon). "StarLogo TNG : the convergence of graphical programming and text processing." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/36904.

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Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (leaves 67-68).
StarLogo TNG is a robust graphical programming environment for secondary students. Despite the educational advantages of graphical programming, TNG has sustained criticism from some who object to the exclusion of a textual language. Recognizing the benefits of text processing and the power of controlling software with a keyboard, I sought to incorporate text-processing techniques into TNG's graphical language. The key component of this work is an innovation dubbed "Typeblocking," by which users construct block code through the use of a keyboard.
by Corey McCaffrey.
M.Eng.and S.B.
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Ganguli, Nitu. "The design considerations for display oriented proportional text editors using bit-mapped graphics display systems /." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66142.

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Lok, Shien-wai. "A galley and page formatter based on relations /." Thesis, McGill University, 1985. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63352.

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Joachims, Thorsten. "Learning to classify text using support vector machines /." Boston [u.a.] : Kluwer Acad. Publ, 2002. http://www.loc.gov/catdir/toc/fy032/2002022127.html.

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Venour, Chris. "A computational model of lexical incongruity in humorous text." Thesis, University of Aberdeen, 2013. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=201735.

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Many theories of humour claim that incongruity is an essential ingredient of humour. How- ever this idea is poorly understood and little work has been done in computational humour to quantify it. For example classifiers which attempt to distinguish jokes from regular texts tend to look for secondary features of humorous texts rather than for incongruity. Similarly most joke generators attempt to recreate structural patterns found in example jokes but do not deliberately endeavour to create incongruity. As in previous research, this thesis develops classifiers and a joke generator which attempt to automatically recognize and generate a type of humour. However the systems described here differ from previous programs because they implement a model of a certain type of humorous incongruity. We focus on a type of register humour we call lexical register jokes in which the tones of individual words are in conflict with each other. Our goal is to create a semantic space that reflects the kind of tone at play in lexical register jokes so that words that are far apart in the space are not simply different but exhibit the kinds of incongruities seen in lexical jokes. This thesis attempts to develop such a space and various classifiers are implemented to use it to distinguish lexical register jokes from regular texts. The best of these classifiers achieved high levels of accuracy when distinguishing between a test set of lexical register jokes and 4 different kinds of regular text. A joke generator which makes use of the semantic space to create original lexical register jokes is also implemented and described in this thesis. In a test of the generator, texts that were generated by the system were evaluated by volunteers who considered them not as humorous as human-made lexical register jokes but significantly more humorous than a set of control (i.e.non- joke) texts. This was an encouraging result which suggests that the vector space is somewhat successful in discovering lexical differences in tone and in modelling lexical register jokes.
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Green, Charles Arthur. "An empirical study on the effects of a collaboration-aware computer system and several communication media alternatives on product quality and time to complete in a co-authoring environment /." This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-01122010-020201/.

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Zobair, Hamza A. "A method for finding common attributes in hetrogenous DoD databases." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Jun%5FZobair.pdf.

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Books on the topic "Text processing (Computer science)"

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Wojciech, Rytter, ed. Text algorithms. New York: Oxford University Press, 1994.

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1950-, Srihari Sargur N., ed. Computer text recognition and error correction. Silver Spring, MD: IEEE Computer Society Press, 1985.

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G, Sampath. An introduction to text processing. Jeffersontown, Ky. (P.O. Box 99752, Jeffersontown 40299): River Valley Pub., 1985.

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Kaare, Christian. The UNIX text processing system. New York: Wiley, 1987.

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Crochemore, Maxime. Text algorithhms. Oxford: Oxford University Press, 1995.

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Bell, Timothy C. Text compression. Englewood Cliffs, N.J: Prentice Hall, 1990.

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D, Smith Peter. An introduction to text processing. Cambridge, Mass: MIT Press, 1990.

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Ransom, Robert. Text and document processing in science and technology. Wilmslow, U.K: Sigma Press, 1987.

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Roland, Kamzelak, ed. Computergestützte Text-Edition. Tübingen: Niemeyer, 1999.

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Buxton, Rosalind. OCR text processing (business professional): Level 1 book : text production, word processing and audio transcription. Edited by Dowson Jill. London: Hodder Education, 2010.

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Book chapters on the topic "Text processing (Computer science)"

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Weik, Martin H. "text processing." In Computer Science and Communications Dictionary, 1773. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_19475.

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Tsintsadze, Magda, Manana Khachidze, and Maia Archuadze. "On Georgian Text Processing Toolkit Development." In Lecture Notes in Computer Science, 3–8. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16500-9_1.

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Nevzorova, Olga, and Vladimir Nevzorov. "Ontology-Driven Processing of Unstructured Text." In Communications in Computer and Information Science, 129–42. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30763-9_11.

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Glazkova, Anna, Valery Kruzhinov, and Zinaida Sokova. "Automatic Text Processing for Historical Research." In Communications in Computer and Information Science, 135–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78273-3_14.

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Fernandes, Natasha, Mark Dras, and Annabelle McIver. "Generalised Differential Privacy for Text Document Processing." In Lecture Notes in Computer Science, 123–48. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17138-4_6.

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Vilares, Jesús, Miguel A. Alonso, and Manuel Vilares. "Morphological and Syntactic Processing for Text Retrieval." In Lecture Notes in Computer Science, 371–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30075-5_36.

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Szeliski, Richard. "Image processing." In Texts in Computer Science, 87–180. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84882-935-0_3.

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Szeliski, Richard. "Image Processing." In Texts in Computer Science, 85–151. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-34372-9_3.

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Morazán, Marco. "List Processing." In Texts in Computer Science, 281–318. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85091-3_13.

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Morazán, Marco. "Interval Processing." In Texts in Computer Science, 331–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85091-3_15.

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Conference papers on the topic "Text processing (Computer science)"

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Quille, Rosa V. E., Caetano Traina, and Jose F. Rodrigues. "Spectral analysis and text processing over the computer science literature." In SAC 2014: Symposium on Applied Computing. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2554850.2555151.

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Gohad, Ameya, Anay Vyawahare, Anmol Gupta, Kashish Sharma, Keyur Dhage, Nilesh Shelke, and Jagdish Patani. "ATS: Auto Text Summarization using Natural Language Processing." In 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE, 2024. http://dx.doi.org/10.1109/sceecs61402.2024.10482227.

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Jing, Bowen. "Neural network structure text processing method based on automatic machine learning." In International Conference on Mathematics, Modeling and Computer Science (MMCS2022), edited by Kun Zhang. SPIE, 2023. http://dx.doi.org/10.1117/12.2670485.

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"Phonetic Speech Perception of Short Text and Information Processing Strategy of Continuum." In 2018 International Conference on Computer Science and Biomedical Engineering. Francis Academic Press, 2018. http://dx.doi.org/10.25236/csbioe.2018.15.

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Zhang, Li, Liubo Ouyang, and Jiahao Qin. "Automatic Detection Method for Software Requirements Text with Language Processing Model." In 2023 6th International Conference on Software Engineering and Computer Science (CSECS). IEEE, 2023. http://dx.doi.org/10.1109/csecs60003.2023.10428155.

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Kavi, Deniz. "Towards Adversarial Genetic Text Generation." In 8th International Conference on Computer Science and Information Technology (CoSIT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110407.

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Text generation is the task of generating natural language, and producing outputs similar to or better than human texts. Due to deep learning’s recent success in the field of natural language processing, computer generated text has come closer to becoming indistinguishable to human writing. Genetic Algorithms have not been as popular in the field of text generation. We propose a genetic algorithm combined with text classification and clustering models which automatically grade the texts generated by the genetic algorithm. The genetic algorithm is given poorly generated texts from a Markov chain, these texts are then graded by a text classifier and a text clustering model. We then apply crossover to pairs of texts, with emphasis on those that received higher grades. Changes to the grading system and further improvements to the genetic algorithm are to be the focus of future research.
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Orellana, Gerardo, Belen Arias, Marcos Orellana, Victor Saquicela, Fernando Baculima, and Nelson Piedra. "A Study on the Impact of Pre-Processing Techniques in Spanish and English Text Classification over Short and Large Text Documents." In 2018 International Conference on Information Systems and Computer Science (INCISCOS). IEEE, 2018. http://dx.doi.org/10.1109/inciscos.2018.00047.

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Kushwaha, Ajay, Hari Ram Sharma, and Asha Ambhaikar. "Selective Encryption using Natural Language Processing for Text data in Mobile Ad hoc Network." In First EAI International Conference on Computer Science and Engineering. EAI, 2017. http://dx.doi.org/10.4108/eai.27-2-2017.152279.

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"Word Segmentation of Hand Written English Text for Improvement of Word Spotting Results." In International Conference on Image Processing, Production and Computer Science. Universal Researchers, 2016. http://dx.doi.org/10.17758/ur.u0316015.

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GuanHong Zhang and Odbal. "Sentence alignment for web page text based on vector space model." In 2012 International Conference on Computer Science and Information Processing (CSIP). IEEE, 2012. http://dx.doi.org/10.1109/csip.2012.6308821.

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Reports on the topic "Text processing (Computer science)"

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Modlo, Yevhenii O., Serhiy O. Semerikov, Pavlo P. Nechypurenko, Stanislav L. Bondarevskyi, Olena M. Bondarevska, and Stanislav T. Tolmachev. The use of mobile Internet devices in the formation of ICT component of bachelors in electromechanics competency in modeling of technical objects. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3264.

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Computer simulation of technical objects and processes is one of the components of the system of professional training of a modern electromechanics engineer. It has been established that despite the fact that mobile Internet devices (MID) are actively used by electrical engineers, the methods of using them in the process of bachelor in electromechanics training is considered only in some domestic scientific studies. The article highlights the components of the methods of using MID in the formation of the ICT component of the competence of the bachelor in electromechanics in modeling of technical objects, providing for students to acquire basic knowledge in the field of Computer Science and modern ICT and skills to use programming systems, math packages, subroutine libraries, and the like. For processing tabular data, it is proposed to use various freely distributed tools that do not significantly differ in functionality, such as Google Sheets, Microsoft Excel, for processing text data – QuickEdit Text Editor, Google Docs, Microsoft Word. For 3D-modeling and viewing the design and technological documentation, the proposed comprehensive use of Autodesk tools in the training process.
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Kiv, Arnold E., Serhiy O. Semerikov, Vladimir N. Soloviev, and Andrii M. Striuk. Second student workshop on computer science & software engineering. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3670.

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This is an introductory text to a collection of selected papers from the Second Student Workshop on Computer Science & Software Engineering (CS&SE@SW 2019), which was held in Kryvyi Rih, Ukraine, on the November 29, 2019. It consists of short summaries of selected papers and some observations about the event and its future.
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Kiv, Arnold E., Serhiy O. Semerikov, Vladimir N. Soloviev, and Andrii M. Striuk. First student workshop on computer science & software engineering. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2879.

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This is an introductory text to a collection of selected papers from the First Student Workshop on Computer Science & Software Engineering (CS&SE@SW 2018), which was held in Kryvyi Rih, Ukraine, on the November 30, 2018. It consists of short summaries of selected papers and some observations about the event and its future.
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Kiv, Arnold E., Serhiy O. Semerikov, Vladimir N. Soloviev, and Andrii M. Striuk. 3rd Workshop for Young Scientists in Computer Science & Software Engineering. CEUR Workshop Proceedings, March 2021. http://dx.doi.org/10.31812/123456789/4355.

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This is an introductory text to a collection of selected papers from the 3rd Workshop for Young Sci- entists in Computer Science & Software Engineering (CS&SE@SW 2020), which was held in Kryvyi Rih, Ukraine, on the November 27, 2020. It consists of short summaries of selected papers and some observations about the event and its future.
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Ків, Арнольд Юхимович, Сергій Олексійович Семеріков, Володимир Миколайович Соловйов, Andrii M. Striuk, and Андрій Миколайович Стрюк. 4th Workshop for Young Scientists in Computer Science & Software Engineering. Криворізький державний педагогічний університет, January 2022. http://dx.doi.org/10.31812/123456789/6979.

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This is an introductory text to a collection of selected papers from the 4th Workshop for Young Scientists in Computer Science & Software Engineering (CS&SE@SW 2021), which was held in Kryvyi Rih, Ukraine, on the December 18, 2021. It consists of short summaries of selected papers and some observations about the event and its future.
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Volkova, Nataliia P., Nina O. Rizun, and Maryna V. Nehrey. Data science: opportunities to transform education. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3241.

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The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business for business analytics and production, in sales for offerings and, for sales forecasting, in marketing for customizing customers, and recommendations on purchasing, digital marketing, in banking and insurance for risk assessment, fraud detection, scoring, and in medicine for disease forecasting, process automation and patient health monitoring, in tourism in the field of price analysis, flight safety, opinion mining etc. However, data science applications in education have been relatively limited, and many opportunities for advancing the fields still unexplored.
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Furey, John, Austin Davis, and Jennifer Seiter-Moser. Natural language indexing for pedoinformatics. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41960.

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The multiple schema for the classification of soils rely on differing criteria but the major soil science systems, including the United States Department of Agriculture (USDA) and the international harmonized World Reference Base for Soil Resources soil classification systems, are primarily based on inferred pedogenesis. Largely these classifications are compiled from individual observations of soil characteristics within soil profiles, and the vast majority of this pedologic information is contained in nonquantitative text descriptions. We present initial text mining analyses of parsed text in the digitally available USDA soil taxonomy documentation and the Soil Survey Geographic database. Previous research has shown that latent information structure can be extracted from scientific literature using Natural Language Processing techniques, and we show that this latent information can be used to expedite query performance by using syntactic elements and part-of-speech tags as indices. Technical vocabulary often poses a text mining challenge due to the rarity of its diction in the broader context. We introduce an extension to the common English vocabulary that allows for nearly-complete indexing of USDA Soil Series Descriptions.
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Zeng, Jing, Qing Liu, Zhengfang Lei, Zhe Sun, and Yang Wang. Evaluation of Integrated Neuromuscular Training on the Recovery of Joint Injury: A Meta-Analysis and Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2021. http://dx.doi.org/10.37766/inplasy2021.12.0136.

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Review question / Objective: This study will provide new evidence for the effect of integrated neuromuscular training on the recovery of joint injury. Information sources: According to the PICOS principle, the third and fourth authors of this paper searched PsycINFO, Science direct, PubMed, Eric, Willey, China Knowledge Network (CNKI) Academic Journal Online Publishing General Library and China Knowledge Network (CNKI) excellent doctoral thesis full-text database by computer to collect relevant research on the impact of INT on joint injury repair. The time limit of injury retrieval is from the establishment of the database to December 2021.
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9

Modlo, Yevhenii O., Serhiy O. Semerikov, Stanislav L. Bondarevskyi, Stanislav T. Tolmachev, Oksana M. Markova, and Pavlo P. Nechypurenko. Methods of using mobile Internet devices in the formation of the general scientific component of bachelor in electromechanics competency in modeling of technical objects. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3677.

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An analysis of the experience of professional training bachelors of electromechanics in Ukraine and abroad made it possible to determine that one of the leading trends in its modernization is the synergistic integration of various engineering branches (mechanical, electrical, electronic engineering and automation) in mechatronics for the purpose of design, manufacture, operation and maintenance electromechanical equipment. Teaching mechatronics provides for the meaningful integration of various disciplines of professional and practical training bachelors of electromechanics based on the concept of modeling and technological integration of various organizational forms and teaching methods based on the concept of mobility. Within this approach, the leading learning tools of bachelors of electromechanics are mobile Internet devices (MID) – a multimedia mobile devices that provide wireless access to information and communication Internet services for collecting, organizing, storing, processing, transmitting, presenting all kinds of messages and data. The authors reveals the main possibilities of using MID in learning to ensure equal access to education, personalized learning, instant feedback and evaluating learning outcomes, mobile learning, productive use of time spent in classrooms, creating mobile learning communities, support situated learning, development of continuous seamless learning, ensuring the gap between formal and informal learning, minimize educational disruption in conflict and disaster areas, assist learners with disabilities, improve the quality of the communication and the management of institution, and maximize the cost-efficiency. Bachelor of electromechanics competency in modeling of technical objects is a personal and vocational ability, which includes a system of knowledge, skills, experience in learning and research activities on modeling mechatronic systems and a positive value attitude towards it; bachelor of electromechanics should be ready and able to use methods and software/hardware modeling tools for processes analyzes, systems synthesis, evaluating their reliability and effectiveness for solving practical problems in professional field. The competency structure of the bachelor of electromechanics in the modeling of technical objects is reflected in three groups of competencies: general scientific, general professional and specialized professional. The implementation of the technique of using MID in learning bachelors of electromechanics in modeling of technical objects is the appropriate methodic of using, the component of which is partial methods for using MID in the formation of the general scientific component of the bachelor of electromechanics competency in modeling of technical objects, are disclosed by example academic disciplines “Higher mathematics”, “Computers and programming”, “Engineering mechanics”, “Electrical machines”. The leading tools of formation of the general scientific component of bachelor in electromechanics competency in modeling of technical objects are augmented reality mobile tools (to visualize the objects’ structure and modeling results), mobile computer mathematical systems (universal tools used at all stages of modeling learning), cloud based spreadsheets (as modeling tools) and text editors (to make the program description of model), mobile computer-aided design systems (to create and view the physical properties of models of technical objects) and mobile communication tools (to organize a joint activity in modeling).
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