Journal articles: '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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Gelbart, Daphne, and J. C. Smith. "The application of automated text processing techniques to legal text management." International Review of Law, Computers & Technology 8, no. 1 (January 1994): 203–10. http://dx.doi.org/10.1080/13600869.1994.9966390.

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Belov, Serey, Daria Zrelova, Petr Zrelov, and Vladimir Korenkov. "Overview of methods for automatic natural language text processing." System Analysis in Science and Education, no. 3 (2020) (September 30, 2020): 8–22. http://dx.doi.org/10.37005/2071-9612-2020-3-8-22.

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This paper provides a brief overview of modern methods and approaches used for automatic processing of text information. In English-language literature, this area of science is called NLP-Natural Language Processing. The very name suggests that the subject of analysis (and for many tasks – and synthesis) are materials presented in one of the natural languages (and for a number of tasks – in several languages simultaneously), i.e. national languages of communication between people. Programming languages are not included in this group. In Russian-language literature, this area is called Computer (or mathematical) linguistics. NLP (computational linguistics) usually includes speech analysis along with text analysis, but in this review speech analysis does not consider. The review used materials from original works, monographs, and a number of articles published the «Open Systems.DBMS» journal.

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Sani, Dian Ahkam, and Muchammad Saifulloh. "Speech to Text Processing for Interactive Agent of Virtual Tour Navigation." International Journal of Artificial Intelligence & Robotics (IJAIR) 1, no. 1 (October 31, 2019): 31. http://dx.doi.org/10.25139/ijair.v1i1.2030.

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The development of science and technology is one way to replace the method of human interaction with computers, one of which is to provide voice input. Conversion of sound into text form with the Backpropagation method can be understood and realized through feature extraction, including the use of Linear Predictive Coding (LPC). Linear Predictive Coding is one way to represent the signal in obtaining the features of each sound pattern. In brief, the way this speech recognition system worked was by inputting human voice through a microphone (analog signal) which then sampled with a sampling speed of 8000 Hz so that it became a digital signal with the assistance of sound card on the computer. The digital signal from the sample then entered the initial process using LPC, so that several LPC coefficients were obtained. The LPC outputs were then trained using the Backpropagation learning method. The results of the learning were classified with a word and stored in a database afterwards. The results of the test were in the form of an introduction program that able display the voice plots. the results of speech recognition with voice recognition percentage of respondents in the database iss 80% of the 100 data in the test in Real Time

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Lu, Ruei-Shan, Hsiu-Yuan Tsao, Hao-Chaing Koong Lin, Yu-Chun Ma, and Cheng-Tung Chuang. "Sentiment Analysis of Brand Personality Positioning Through Text Mining." Journal of Information Technology Research 12, no. 3 (July 2019): 93–103. http://dx.doi.org/10.4018/jitr.2019070106.

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This article uses text mining and a Chinese word segmentation program developed by the Chinese Knowledge and Information Processing Group in Taiwan's Academia Sinica to analyze Facebook posts from 14 e-commerce companies. In addition, a list of keywords representing brand personalities is analyzed to reveal key factors affecting which social media posts attract consumers' attention. This research uses statistical analysis with a nonmanual questionnaire that is efficient and based on computer science to provide a reference for businesses operating Facebook fan pages and internet marketing.

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Fonseca, Claudia Aparecida, Marcus Vinícius Carvalho Guelpeli, and Rafael Santiago de Souza Netto. "Representation of structured data of the text genre as a technique for automatic text processing." Texto Livre 15 (January 27, 2022): e35445. http://dx.doi.org/10.35699/1983-3652.2022.35445.

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The present article was developed in the field of Natural Language Processing and Language Studies based on a corpus compiled by computational tools. This study is based on the assumption that it is helpful to trace a close relationship between corpus generation/annotation and the assessment of the constitutive elements of the text genre source. It aims to demonstrate, through specific studies of structured data from the text genre ‘scientific article’, alternatives to automatic text processing techniques. In order to reach the intended goal, the authors created a computational model for the compilation of a linguistic, specialized Corpus, representative of the genre Scientific Article - CorpACE. The object of study includes the constitutive elements of scientific articles, marked in XML, extracted and collected from the SciELO-Scientific Electronic Library On-line database. The final product was a database obtained with information extracted and structured in XML format, which designates and identifies the markups of the genre being analyzed and is available for many tools and applications. The results demonstrate how the representation of constitutive elements of the genre can condense available information with hierarchical and dynamic processes built during the compilation. At the end of the study, it is believed that more research will be required for bringing Language Science and Computer Science closer with emphasis on NLP in the attempt to represent and manipulate linguistic knowledge in its many levels – morphological, syntactic, semantic and discursive – in order to improve implementation and manipulation of automatic text processing.

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Jain, Vinay Kumar, Shishir Kumar, and Steven Lawrence Fernandes. "Extraction of emotions from multilingual text using intelligent text processing and computational linguistics." Journal of Computational Science 21 (July 2017): 316–26. http://dx.doi.org/10.1016/j.jocs.2017.01.010.

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Porwoł, Monika. "NLP ‘RECIPES’ FOR TEXT CORPORA: APPROACHES TO COMPUTING THE PROBABILITY OF A SEQUENCE OF TOKENS." Studia Philologica 2, no. 15 (2020): 6–13. http://dx.doi.org/10.28925/2311-2425.2021.151.

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Investigation in the hybrid architectures for Natural Language Processing (NLP) requires overcoming complexity in various intellectual traditions pertaining to computer science, formal linguistics, logic, digital humanities, ethical issues and so on. NLP as a subfield of computer science and artificial intelligence is concerned with interactions between computers and human (natural) languages. It is used to apply machine learning algorithms to text (and speech) in order to create systems, such as: machine translation (converting from text in a source language to text in a target language), document summarization (converting from long texts into short texts), named entity recognition, predictive typing, et cetera. Undoubtedly, NLP phenomena have been implanted in our daily lives, for instance automatic Machine Translation (MT) is omnipresent in social media (or on the world wide web), virtual assistants (Siri, Cortana, Alexa, and so on) can recognize a natural voice or e-mail services use detection systems to filter out some spam messages. The purpose of this paper, however, is to outline the linguistic and NLP methods to textual processing. Therefore, the bag-of-n-grams concept will be discussed here as an approach to extract more details about the textual data in a string of a grouped words. The n-gram language model presented in this paper (that assigns probabilities to sequences of words in text corpora) is based on findings compiled in Sketch Engine, as well as samples of language data processed by means of NLTK library for Python. Why would one want to compute the probability of a word sequence? The answer is quite obvious – in various systems for performing tasks, the goal is to generate texts that are more fluent. Therefore, a particular component is required, which computes the probability of the output text. The idea is to collect information how frequently the n-grams occur in a large text corpus and use it to predict the next word. Counting the number of occurrences can also envisage certain drawbacks, for instance there are sometimes problems with sparsity or storage. Nonetheless, the language models and specific computing ‘recipes’ described in this paper can be used in many applications, such as machine translation, summarization, even dialogue systems, etc. Lastly, it has to be pointed out that this piece of writing is a part of an ongoing work tentatively termed as LADDER (Linguistic Analysis of Data in the Digital Era of Research) that touches upon the process of datacization[1] that might help to create an intelligent system of interdisciplinary information.

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Tabuchi, Naoshi, Eijiro Sumii, and Akinori Yonezawa. "Regular Expression Types for Strings in a Text Processing Language." Electronic Notes in Theoretical Computer Science 75 (February 2003): 95–113. http://dx.doi.org/10.1016/s1571-0661(04)80781-3.

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Coombs, James H., Allen H. Renear, and Steven J. DeRose. "Markup systems and the future of scholarly text processing." Communications of the ACM 30, no. 11 (November 1987): 933–47. http://dx.doi.org/10.1145/32206.32209.

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Sokol, Volodymyr, Vitalii Krykun, Mariia Bilova, Ivan Perepelytsya, Volodymyr Pustovarov, and Volodymyr Pustovarov. "TOPIC SEGMENTATION METHODS COMPARISON ON COMPUTER SCIENCE TEXTS." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 2 (6) (December 28, 2021): 59–66. http://dx.doi.org/10.20998/2079-0023.2021.02.10.

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The demand for the creation of information systems that simplifies and accelerates work has greatly increased in the context of the rapidinformatization of society and all its branches. It provokes the emergence of more and more companies involved in the development of softwareproducts and information systems in general. In order to ensure the systematization, processing and use of this knowledge, knowledge managementsystems are used. One of the main tasks of IT companies is continuous training of personnel. This requires export of the content from the company'sknowledge management system to the learning management system. The main goal of the research is to choose an algorithm that allows solving theproblem of marking up the text of articles close to those used in knowledge management systems of IT companies. To achieve this goal, it is necessaryto compare various topic segmentation methods on a dataset with a computer science texts. Inspec is one such dataset used for keyword extraction andin this research it has been adapted to the structure of the datasets used for the topic segmentation problem. The TextTiling and TextSeg methods wereused for comparison on some well-known data science metrics and specific metrics that relate to the topic segmentation problem. A new generalizedmetric was also introduced to compare the results for the topic segmentation problem. All software implementations of the algorithms were written inPython programming language and represent a set of interrelated functions. Results were obtained showing the advantages of the Text Seg method incomparison with TextTiling when compared using classical data science metrics and special metrics developed for the topic segmentation task. Fromall the metrics, including the introduced one it can be concluded that the TextSeg algorithm performs better than the TextTiling algorithm on theadapted Inspec test data set.

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Willett, Peter. "Textual and chemical information processing using parallel computer hardware." Journal of Information Science 15, no. 4-5 (August 1989): 223–36. http://dx.doi.org/10.1177/016555158901500405.

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This paper discusses the use of parallel computer hardware to increase the efficiency of processing in databases of text and chemical structures. After a general introduction to parallelism, two types of parallel computer are described: the ICL Distrib uted Array Processor and the INMOS Transputer. Experimen tal results are presented of the use of the DAP for cluster analysis, of the transputer for chemical substructure and maxi mal common substructure searching, and of both machines for text retrieval.

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Joshi, Deepali, Harsh Zanwar, Keyur Soni, Sanika Yadav, and Priya Wankhade. "Analysis of Different Text Features Using NLP." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 2244–48. http://dx.doi.org/10.22214/ijraset.2023.50147.

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Abstract: A single letter can create a word, then that single word can create a paragraph and many paragraphs together make a perfect bound of a text collection, make a perfect article. These texts, these words can be said a way through which one expresses themselves. Be it for personal reasons, or professional, Texts do play an important role to convey the feelings. So, with these texts, many operations can be performed on it. Be it making a very large paragraph shorter - as is done in para summarizer, be it changing the language of a given text - as in language translator, be it automatically guessing of the next word - as in automatic word generator, or be it the spelling a grammar corrector. Here, NLP takes its role. Natural Language Processing i.e., NLP includes studies of how computers and humans communicates in a particular language, more precisely natural language. It is a field that blends computer science, linguistics, and machine learning. NLP aspires to enable computers to understand and generate human language. There are many more tasks one can perform with the text given, to get a more fruitful output of it. In this paper, work done on some of these operations or features of the texts are discussed and summarized.

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Sadirmekova, Zhanna, Jamalbek Tussupov, Aslanbek Murzakhmetov, Gulkiz Zhidekulova, Aigul Tungatarova, Murat Tulenbayev, Shynar Akhmetzhanova, Zhanar Altynbekova, and Gauhar Borankulova. "Ontology engineering of automatic text processing methods." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (December 1, 2023): 6620. http://dx.doi.org/10.11591/ijece.v13i6.pp6620-6628.

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Currently, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject area (SSA). Practice has shown that using ontology design patterns is effective in developing the ontology of scientific subject areas. This is due to the fact that scientific subject areas ontology, as a rule, contains a large number of typical fragments that are well described by patterns of ontology design. In the paper, we present an approach to ontology engineering of automatic text processing methods based on ontology design patterns. In order to get an ontology that would describe automatic text processing sufficiently fully, it is required to process a large number of scientific publications and information resources containing information from modeling area. It is possible to facilitate and speed up the process of updating ontology with information from such sources by using lexical and syntactic patterns of ontology design. Our ontology of automatic text processing will become the conceptual basis of an intelligent information resource on modern methods of automatic text processing, which will provide systematization of all information on these methods, its integration into a single information space, convenient navigation through it, as well as meaningful access to it.

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Prihatini, Putu Manik, and I. Ketut Suryawan. "Text Processing Application for Indonesian Documents." Advanced Science Letters 23, no. 12 (December 1, 2017): 12186–89. http://dx.doi.org/10.1166/asl.2017.10598.

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Rohit Kumar Yadav, Aanchal Madaan, and Janu. "Comprehensive analysis of natural language processing." Global Journal of Engineering and Technology Advances 19, no. 1 (April 30, 2024): 083–90. http://dx.doi.org/10.30574/gjeta.2024.19.1.0058.

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Natural Language Processing (NLP) is a fascinating field of study that teaches computers to understand and use human language. This means that computers can read, write, and even translate text just like humans. NLP has many practical uses, such as categorizing text, identifying the tone of language, recognizing names in text, translating languages, and answering questions. NLP has come a long way since it was first developed. In the past, it relied on strict rules to understand language, but now it uses advanced techniques like machine learning and deep learning to understand text. However, there are still some challenges in NLP, such as understanding the meaning of words in context and considering cultural differences. Despite these challenges, NLP is being used in many different areas, from healthcare and finance to education and customer service. NLP is transforming the way humans interact with computers and is making it easier to extract important information from large amounts of text.

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L, Anusha, and Nagaraja G. S. "Outlier Detection in High Dimensional Data." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 128–30. http://dx.doi.org/10.35940/ijeat.e2675.0610521.

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Artificial intelligence (AI) is the science that allows computers to replicate human intelligence in areas such as decision-making, text processing, visual perception. Artificial Intelligence is the broader field that contains several subfields such as machine learning, robotics, and computer vision. Machine Learning is a branch of Artificial Intelligence that allows a machine to learn and improve at a task over time. Deep Learning is a subset of machine learning that makes use of deep artificial neural networks for training. The paper proposed on outlier detection for multivariate high dimensional data for Autoencoder unsupervised model.

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Drofova, Irena, Milan Adamek, Pavel Stoklasek, Martin Ficek, and Jan Valouch. "Application 3D Forensic Science in a Criminal Investigation." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 20 (March 2, 2023): 59–65. http://dx.doi.org/10.37394/23209.2023.20.8.

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This manuscript discusses the modern approach and application of 3D digital imaging in forensic science. It presents the basic principles and approaches of 3D modeling methods. Selected methods of image capture and its subsequent processing into a 3D model are applied to a specific object. This object is captured by a mobile phone camera, a LiDar sensor, and a 3D scanner for further image processing for different desired image outputs. The text describes the photogrammetry method, the workflow with the LiDar sensor, and the 3D model of the object intended for 3D printing. The paper discusses the potential of the selected methods and their application in forensic sciences.

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HUO, YUMEI, JOSEPH Y. T. LEUNG, and XIN WANG. "PREEMPTIVE SCHEDULING ALGORITHMS WITH NESTED PROCESSING SET RESTRICTION." International Journal of Foundations of Computer Science 20, no. 06 (December 2009): 1147–60. http://dx.doi.org/10.1142/s012905410900708x.

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We consider the problem of preemptively scheduling n independent jobs {J1, J2, …, Jn} on m parallel machines {M1, M2, …, Mm}, where each job Jj can only be processed on a prespecified subset [Formula: see text] of machines called its processing set. The machines are linearly ordered, and the processing set of Jj is specified by two machine indexes aj and bj; i.e., [Formula: see text]. The processing sets are nested; i.e., for i ≠ j, we have [Formula: see text], or [Formula: see text], or [Formula: see text]. Our goal is to minimize the makespan. We first give an O(n log n)-time algorithm to find an optimal schedule. We then give an O(mn + n log n)-time algorithm to find a maximal schedule, where a schedule is said to be maximal if it processes as much work as any other schedule in any time interval [0, t], t > 0.

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Ali, Zahraa, and Safaa O. Al-mamory. "Identifying Researchers’ Interest using Text Mining." Iraqi Journal for Computers and Informatics 50, no. 1 (June 1, 2024): 34–45. http://dx.doi.org/10.25195/ijci.v50i1.475.

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Researchers' interests and academic journals are crucial for advancing scientific inquiry. Journals serve as platforms for sharing and validating discoveries, fostering a symbiotic relationship that advances our collective understanding and pushes the boundaries of human knowledge. Journals, which encompass natural edge research and establish benchmarks for academic rigor. In this paper, an analysis, using text mining, of the publications of Iraqi researchers in scientific journals is used to extract the researcher's interest. In more detail, this paper utilizes the following technologies: pre-processing (tokenization, POS (“Part Of Speech”), normalization, case folding, lemmatization) – filtering (stop word elimination) - feature Extraction (TF-IDF), as well as classification using deep neural network classifier (DNNC), to address the problem of identifying the researcher's interests through texts (title &abstract) analysis. The Iraqi researchers’ data in the field of computer science from the years 2010-2022. As obtained from the Scopus repository, a total of 1170 papers were collected via API- key and scrubber depending on the keyword of computer science and the year. Furthermore, these papers were manually classified based on the hierarchical classification of the ACM journal. Finally, the best results obtained from a classification using DNN and TF-IDF as classifying terms achieved a precision of 90%, Recall of 90%, f1-score of 90%, and accuracy of 90%.

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Qiao, Jianfeng, Xingzhi Yan, and Shuran Lv. "Natural Language Processing Using Neighbour Entropy-based Segmentation." Journal of Computing and Information Technology 29, no. 2 (July 4, 2022): 113–31. http://dx.doi.org/10.20532/cit.2021.1005393.

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In natural language processing (NLP) of Chinese hazard text collected in the process of hazard identification, Chinese word segmentation (CWS) is the first step to extracting meaningful information from such semi-structured Chinese texts. This paper proposes a new neighbor entropy-based segmentation (NES) model for CWS. The model considers the segmentation benefits of neighbor entropies, adopting the concept of "neighbor" in optimization research. It is defined by the benefit ratio of text segmentation, including benefits and losses of combining the segmentation unit with more information than other popular statistical models. In the experiments performed, together with the maximum-based segmentation algorithm, the NES model achieves a 99.3% precision, 98.7% recall, and 99.0% f-measure for text segmentation; these performances are higher than those of existing tools based on other seven popular statistical models. Results show that the NES model is a valid CWS, especially for text segmentation requirements necessitating longer-sized characters. The text corpus used comes from the Beijing Municipal Administration of Work Safety, which was recorded in the fourth quarter of 2018.

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Smith, Glenn Gordon, Robert Haworth, and Slavko Žitnik. "Computer Science Meets Education: Natural Language Processing for Automatic Grading of Open-Ended Questions in eBooks." Journal of Educational Computing Research 58, no. 7 (May 28, 2020): 1227–55. http://dx.doi.org/10.1177/0735633120927486.

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We investigated how Natural Language Processing (NLP) algorithms could automatically grade answers to open-ended inference questions in web-based eBooks. This is a component of research on making reading more motivating to children and to increasing their comprehension. We obtained and graded a set of answers to open-ended questions embedded in a fiction novel written in English. Computer science students used a subset of the graded answers to develop algorithms designed to grade new answers to the questions. The algorithms utilized the story text, existing graded answers for a given question and publicly accessible databases in grading new responses. A computer science professor used another subset of the graded answers to evaluate the students’ NLP algorithms and to select the best algorithm. The results showed that the best algorithm correctly graded approximately 85% of the real-world answers as correct, partly correct, or wrong. The best NLP algorithm was trained with questions and graded answers from a series of new text narratives in another language, Slovenian. The resulting NLP algorithm model was successfully used in fourth-grade language arts classes for providing feedback to student answers on open-ended questions in eBooks.

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ZADROZNY, S., and J. KACPRZYK. "Computing with words for text processing: An approach to the text categorization." Information Sciences 176, no. 4 (February 22, 2006): 415–37. http://dx.doi.org/10.1016/j.ins.2005.07.017.

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Xue-Meng Du, Xue-Meng Du, and Ji-Cheng Yang Xue-Meng Du. "A Text Analysis Method for Student Learning Feedback on Network Teaching Platform Based on Natural Language Processing." 電腦學刊 35, no. 1 (February 2024): 177–84. http://dx.doi.org/10.53106/199115992024023501013.

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With the emergence and end of the COVID-19, online learning has become an irreplaceable way of learning. In order to promote the improvement and enhancement of online curriculum resources and increase the learning effect of students, the content of curriculum evaluation is an important reference for the direction of curriculum improvement. Therefore, this article focuses on the student learning feedback of course resources. Firstly, through data collection algorithms, effective evaluation information is crawled, and then based on the collected information, the course evaluation text is annotated and classified, forming a reasonable corpus. Finally, through feature collection and sentiment analysis algorithms, sentiment analysis is performed on the evaluation content, effectively distinguishing between positive and negative evaluations, and guiding teachers to improve the course content.

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Dagienė, Valentina. "Moksleivių kompiuterinio raštingumo standarto metodologinis pagrindimas." Lietuvos matematikos rinkinys 42 (December 20, 2002): 219–23. http://dx.doi.org/10.15388/lmr.2002.32891.

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This paper discusses the computer literacy standard of students with respect to the European Computer Driving License (ECDL) and general informatics curricula of comprehensive schools. Some aspects of relations to ECDL Start modules are analyzed. The main attention is paid to discuss the virtue attitude of using the information and communication technology as well as to general capabilities related to computer literacy. The main propositions are presented and motivated. The paper also deals with the main fields of computer literacy topics: Basic principles and concepts of applying computers (1), Basics of information processing (2), Text handling and information presentation (3), Web and electronic mail (4), Introducing spreadsheet and data base (5), Social, juridical and ethical aspects (6).

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Lange, Dale L., Richard Raschio, and Paul Wieser. "AN INFORMATION PROCESSING MODEL FOR COMPUTER-ASSISTED INSTRUCTION FOR FOREIGN LANGUAGE READING." CALICO Journal 3, no. 2 (January 14, 2013): 31–37. http://dx.doi.org/10.1558/cj.v3i2.31-37.

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This article presents the rationale behind a model for the use of the computer in the development of reading comprehension. Basic assumptions concerning reading are delineated. The model is explained as having three basic components: intake (text processing and text comprehension), personalization, andextension. The operationalization of the model is also described.

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Chornenkyi, Oleksandr. "USE OF INFORMATION AND COMMUNICATION TECHNOLOGIES FOR POLITICAL SCIENCE RESEARCH." 42, no. 42 (December 30, 2022): 38–44. http://dx.doi.org/10.26565/2220-8089-2022-42-06.

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The article shows a perspective using information and communication technologies for the amplification of the political processes research methodology. It considered the evolving of research with computational techniques using, complications and a variety of possible approaches. It gives information about using simulation modeling, especially the autonomous adaptive agent method for the research related to the course of political events prognostication. It shows the possibilities of computer modeling for the analysis of complex dynamic systems in which decision-making at the micro level changes the system as a whole. In the article are noted the advantages and disadvantages of simulation modeling for political science research. It is stressed that the internet and social network development is important for modern scientists and gives examples of using social networks as a field and tool for political science analysis. It is noted that the use of such an approach can be an important addition to classical methods. It describes in short the possibilities of «Big Data analysis» for political science and stressed the advantages of the method for research conducting. The text provides information about the «text as data» method for automatically mining and analytical processing of large-scale textual information. It gives an example of the “text as data” used and is noted that the proposed method is useful for comparative analysis. It shows the possibilities of using the method of automatic text analysis not only for processing modern information in digital form but also for the information contained in printed sources using computer optical text recognition. At the same time describes in short, the limitations and disadvantages of this method. Conclusions are drawn that information and communication technologies expands the methodology of political science research, improves efficiency and reliability of conclusions.

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Pikuliak, Matúš, Marián Šimko, and Mária Bieliková. "Cross-lingual learning for text processing: A survey." Expert Systems with Applications 165 (March 2021): 113765. http://dx.doi.org/10.1016/j.eswa.2020.113765.

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Shutova, Ekaterina, Simone Teufel, and Anna Korhonen. "Statistical Metaphor Processing." Computational Linguistics 39, no. 2 (June 2013): 301–53. http://dx.doi.org/10.1162/coli_a_00124.

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Metaphor is highly frequent in language, which makes its computational processing indispensable for real-world NLP applications addressing semantic tasks. Previous approaches to metaphor modeling rely on task-specific hand-coded knowledge and operate on a limited domain or a subset of phenomena. We present the first integrated open-domain statistical model of metaphor processing in unrestricted text. Our method first identifies metaphorical expressions in running text and then paraphrases them with their literal paraphrases. Such a text-to-text model of metaphor interpretation is compatible with other NLP applications that can benefit from metaphor resolution. Our approach is minimally supervised, relies on the state-of-the-art parsing and lexical acquisition technologies (distributional clustering and selectional preference induction), and operates with a high accuracy.

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Zheng, Zihui. "Logical Intelligent Detection Algorithm of Chinese Language Articles Based on Text Mining." Mobile Information Systems 2021 (December 16, 2021): 1–10. http://dx.doi.org/10.1155/2021/8115551.

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With the advent of the big data era and the rapid development of the Internet industry, the information processing technology of text mining has become an indispensable role in natural language processing. In our daily life, many things cannot be separated from natural language processing technology, such as machine translation, intelligent response, and semantic search. At the same time, with the development of artificial intelligence, text mining technology has gradually developed into a research hotspot. There are many ways to realize text mining. This paper mainly describes the realization of web text mining and the realization of text structure algorithm based on HTML through a variety of methods to compare the specific clustering time of web text mining. Through this comparison, we can also get which web mining is the most efficient. The use of WebKB datasets for many times in experimental comparison also reflects that Web text mining for the Chinese language logic intelligent detection algorithm provides a basis.

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Badea, Daniel Onuț, Doru Costin Darabont, Dominic Bucerzan, Alina Trifu, Eduard Smîdu, Eugenia Bulboacă, and Vergilică Haralambie. "Occupational safety issues related to computer equipment installation, maintenance and use." MATEC Web of Conferences 354 (2022): 00001. http://dx.doi.org/10.1051/matecconf/202235400001.

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This paper presents the findings of an ongoing INCDPM project developed in collaborations with BB Computers that addresses the occupational safety issues related to computer equipment installation, maintenance and use, with emphasis on risk identification. The method used was The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). Using the chart flow and the list of items of this method, a literature search was conducted in Science Direct Freedom Collection, Elsevier database, Web of Science - Core Collection, Springer Link Journals. Keywords such as occupational risks, computer equipment installation, computer maintenance were used to retrieve relevant studies which explicitly reported on occupational risks related to computer equipment installation, maintenance and use. The literature search yielded 900 references, of which 20 articles were selected for full-text screening as specified by the inclusion criteria, and ultimately 10 were included in this review. It was developed an extensive and comprehensive list of occupational risks related to computer equipment installation, maintenance and use. After a classification of the equipment in calculation and processing equipment, electronic displays, printer, special printers network equipment the main risks identified in all categories mentioned above are as follows: electric shock, stress, high workload, slips and trips.

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Alsmadi, Izzat, Nura Aljaafari, Mahmoud Nazzal, Shadan Alhamed, Ahmad H. Sawalmeh, Conrado P. Vizcarra, Abdallah Khreishah, et al. "Adversarial Machine Learning in Text Processing: A Literature Survey." IEEE Access 10 (2022): 17043–77. http://dx.doi.org/10.1109/access.2022.3146405.

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Othman, Mohamed Tahar Ben, Mohammed Abdullah Al-Hagery, and Yahya Muhammad El Hashemi. "Arabic Text Processing Model: Verbs Roots and Conjugation Automation." IEEE Access 8 (2020): 103913–23. http://dx.doi.org/10.1109/access.2020.2999259.

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Beishuizen, Jos, Evelien Stout Jesdijk, and Anneke Zanting. "Using Hypertext for Studying and Information Search." Journal of Educational Computing Research 15, no. 4 (December 1996): 289–316. http://dx.doi.org/10.2190/f643-j6uw-qcfn-0jmu.

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A text in hypertext format is a database of text units without any pre-defined order. Concepts maps, text links, and other navigation tools enable the user to explore the database. This study explored the conditions for making hypertext a better study environment than a traditional linear text. Two tasks were investigated: an open exam preparation task and a closed search task. Within the context of the latter task the influence of the student's learning style was traced. For open study tasks, like preparing for an examination, hypertext does not provide clear advantages over a linear text with a table of contents and an index. The first experiment showed that in the hypertext condition more time was spent on actually studying text units explaining important topics. However, there was no increase in text comprehension as compared with a linear text condition. The potential advantages of hypertext may be better utilized in closed search tasks, in which an answer to a particular question has to be found. Because hypertext puts heavier cognitive demands on the student, the quality of the learning style of the student is crucial to success. In the second experiment, we found that both deep processing students and surface processing students were able to find a requested text unit in a hypertext unit in a hypertext database, provided that their regulation style matches their processing style. That is, deep processing students should act on the basis of internal control, whereas surface processing should seek external guidance. The availability of local navigation facilities (like text links or facilities for full text search) contributes to the usability of hypertext, in particular for those users who prefer a surface processing style. However, because surface processing students are vulnerable to losing track, they need external guidance to support their search attempts.

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Noor, Noor Sattar, Dalal Abdulmohsin Hammood, Ali Al-Naji, and Javaan Chahl. "A Fast Text-to-Image Encryption-Decryption Algorithm for Secure Network Communication." Computers 11, no. 3 (March 9, 2022): 39. http://dx.doi.org/10.3390/computers11030039.

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Data security is the science of protecting data in information technology, including authentication, data encryption, data decryption, data recovery, and user protection. To protect data from unauthorized disclosure and modification, a secure algorithm should be used. Many techniques have been proposed to encrypt text to an image. Most past studies used RGB layers to encrypt text to an image. In this paper, a Text-to-Image Encryption-Decryption (TTIED) algorithm based on Cyan, Magenta, Yellow, Key/Black (CMYK) mode is proposed to improve security, capacity, and processing time. The results show that the capacity increased from one to four times compared to RGB mode. Security was also improved due to a decrease in the probability of an adversary discovering keys. The processing time ranged between 0.001 ms (668 characters) and 31 s (25 million characters), depending on the length of the text. The compression rate for the encrypted file was decreased compared to WinRAR. In this study, Arabic and English texts were encrypted and decrypted.

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Price, Kathleen J., Min Lin, Jinjuan Feng, Rich Goldman, Andrew Sears, and Julie Jacko. "Nomadic Speech-Based Text Entry: A Decision Model Strategy for Improved Speech to Text Processing." International Journal of Human-Computer Interaction 25, no. 7 (September 25, 2009): 692–706. http://dx.doi.org/10.1080/10447310902964132.

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Al Smadi, Takialddin, and Sallam Al Zoubi. "Modern Technology for Image processing and Computer vision -A Review." Journal of Advanced Sciences and Engineering Technologies 1, no. 2 (December 29, 2021): 17–23. http://dx.doi.org/10.32441/jaset.01.02.02.

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This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field. The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application. In this work a various subjects of image processing and computer vision will be demonstrated, these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented, where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality. © 2018 JASET, International Scholars and Researchers Association

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Garg, Neha, and Kamlesh Sharma. "Text pre-processing of multilingual for sentiment analysis based on social network data." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (February 1, 2022): 776. http://dx.doi.org/10.11591/ijece.v12i1.pp776-784.

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Sentiment analysis (SA) is an enduring area for research especially in the field of text analysis. Text pre-processing is an important aspect to perform SA accurately. This paper presents a text processing model for SA, using natural language processing techniques for twitter data. The basic phases for machine learning are text collection, text cleaning, pre-processing, feature extractions in a text and then categorize the data according to the SA techniques. Keeping the focus on twitter data, the data is extracted in domain specific manner. In data cleaning phase, noisy data, missing data, punctuation, tags and emoticons have been considered. For pre-processing, tokenization is performed which is followed by stop word removal (SWR). The proposed article provides an insight of the techniques, that are used for text pre-processing, the impact of their presence on the dataset. The accuracy of classification techniques has been improved after applying text pre-processing and dimensionality has been reduced. The proposed corpus can be utilized in the area of market analysis, customer behaviour, polling analysis, and brand monitoring. The text pre-processing process can serve as the baseline to apply predictive analysis, machine learning and deep learning algorithms which can be extended according to problem definition.

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Ahadi, Alireza, Abhay Singh, Matt Bower, and Michael Garrett. "Text Mining in Education—A Bibliometrics-Based Systematic Review." Education Sciences 12, no. 3 (March 15, 2022): 210. http://dx.doi.org/10.3390/educsci12030210.

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Advances in Information Technology (IT) and computer science have without a doubt had a significant impact on our daily lives. The past few decades have witnessed the advancement of IT enabled processes in generating actionable insights in various fields, encouraging research based applications of modern Data Science methods. Among many other fields, education research has also been adopting different analytical approaches to advance the state of education systems. Moreover, developments in software engineering and web-based applications have made collection of education data possible at large scales. This systematic review aims to explore the 21st century’s state of the art applications of text mining methods used in the field of education. We analyse the metadata of all publications that use text mining or natural language processing in educational settings to report on the key themes of application of text mining methods in educational studies providing an overview of the current state of the art and the future directions for research and applications.

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Barkovska, Olesia, Viktor Khomych, and Oleksandr Nastenko. "RESEARCH OF THE TEXT PROCESSING METHODS IN ORGANIZATION OF ELECTRONIC STORAGES OF INFORMATION OBJECTS." Innovative Technologies and Scientific Solutions for Industries, no. 1 (19) (April 26, 2022): 5–12. http://dx.doi.org/10.30837/itssi.2022.19.005.

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The subject matter of the article is electronic storage of information objects (IO) ordered by specified rules at the stage of accumulation of qualification thesis and scientific work of the contributors of the offered knowledge exchange system provided to the system in different formats (text, graphic, audio). Classified works of contributors of the system are the ground for organization of thematic rooms for discussion to spread scientific achievements, to adopt new ideas, to exchange knowledge and to look for employers or mentors in different countries. The goal of the work is to study the libraries of text processing and analysis to speed-up and increase accuracy of the scanned text documents classification in the process of serialized electronic storage of information objects organization. The following tasks are: to study the text processing methods on the basis of the proposed generalized model of the system of classification of scanned documents with the specified location of the block of text processing and analysis; to investigate the statistics of change in the execution time of the developed parallel modification of the methods of the word processing module for the system with shared memory for collections of text documents of different sizes; analyze the results. The methods used are the following: parallel digital sorting methods, methods of mathematical statistics, linguistic methods of text analysis. The following results were obtained: in the course of the research fulfillment the generalized model of the scanned documents classification system that consist of image processing unit and text processing unit that include unit of the scanned image previous processing; text detection unit; previous text processing; compiling of the frequency dictionary; text proximity detection was offered. Conclusions: the proposed parallel modification of the previous text processing unit gives acceleration up to 3,998 times. But, at a very high computational load (collection of 18144 files, about 1100 MB), the resources of an ordinary multiprocessor-based computer with the shared memory obviously is not enough to solve such problems in the mode close to real time.

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Huan, Junrong. "Research on the Application of Artificial Intelligence in Image and Text Database Retrieval." Frontiers in Computing and Intelligent Systems 2, no. 1 (November 23, 2022): 39–41. http://dx.doi.org/10.54097/fcis.v2i1.2708.

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In the graphic database, the difficulty of query processing lies in how to query the contents of various data, that is, content-based retrieval, which is an effective means and an important technology to realize multimedia data retrieval. In graphic database, the difficulty of query processing lies in how to query the content based on unformatted data, that is, content-based retrieval, which is an effective means and an important technology to realize multimedia data retrieval. Intelligent information retrieval (IR) system is an intelligent computer IR system, which simulates the thinking process and intelligent activities of human beings about information processing, realizes the storage, retrieval and reasoning of information knowledge, and provides intelligent assistance to users. This paper analyzes the problems of image and text database retrieval driven by big data, explores the application effect of artificial intelligence (AI) in IR driven by big data, promotes the innovation and transformation of modern science and technology, and realizes the sustainable development of our society.

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

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