Relevant bibliographies by topics / Text-based

Contents

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

Academic literature on the topic 'Text-based'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 June 2025

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Journal articles on the topic "Text-based"

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SI, Qin, Li ZHANG, and De-liang LIAN. "Text watermarking based on text feature." Journal of Computer Applications 29, no. 9 (2009): 2348–50. http://dx.doi.org/10.3724/sp.j.1087.2009.02348.

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Farah, Qasim Ahmed Alyousuf, and Din Roshidi. "Analysis review on feature-based and word-rule based techniques in text steganography." Bulletin of Electrical Engineering and Informatics 9, no. 2 (2020): 764–70. https://doi.org/10.11591/eei.v9i2.2069.

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This paper presents several techniques used in text steganography in term of feature-based and word-rule based. Additionally, it analyses the performance and the metric evaluation of the techniques used in text steganography. This paper aims to identify the main techniques of text steganography, which are feature-based, and word-rule based, to recognize the various techniques used with them. As a result, the primary technique used in the text steganography was feature-based technique due to its simplicity and secured. Meanwhile, the common parameter metrics utilized in text steganography were security, capacity, robustness, and embedding time. Future efforts are suggested to focus on the methods used in text steganography.
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D., Mhamdi. "Job Recommendation System based on Text Analysis." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 1025–30. http://dx.doi.org/10.5373/jardcs/v12sp4/20201575.

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Hussain Zargar, Khalid, and Manzoor Ahmad Chachoo. "Content Based Text Classification Using Morkov Models." International Journal of Scientific Engineering and Research 3, no. 6 (2015): 48–52. https://doi.org/10.70729/ijser15239.

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Lockwood, Robert, and Kevin Curran. "Text based steganography." International Journal of Information Privacy, Security and Integrity 3, no. 2 (2017): 134. http://dx.doi.org/10.1504/ijipsi.2017.088700.

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Lockwood, Robert, and Kevin Curran. "Text based steganography." International Journal of Information Privacy, Security and Integrity 3, no. 2 (2017): 134. http://dx.doi.org/10.1504/ijipsi.2017.10009581.

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YOSHIMI, TAKEHIKO, JIRI JELINEK, OSAMU NISHIDA, NAOYUKI TAMURA, and HARUO MURAKAMI. "Text Analysis based on Text-Wide Grammar." Journal of Natural Language Processing 4, no. 1 (1997): 3–21. http://dx.doi.org/10.5715/jnlp.4.3.

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Li, De, Xue Zhe Jin, and LiHua Cui. "Text recognition algorithm based on text features." International Journal of Multimedia and Ubiquitous Engineering 11, no. 5 (2016): 209–20. http://dx.doi.org/10.14257/ijmue.2016.11.5.19.

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Chatterjee, Ayan, Gourab Dolui, and Dr Uttam Kumar Roy. "Text Based Steganography – A Theoritical Proposal of Text Based Hiding Strategy." International Journal of Scientific and Engineering Research 6, no. 11 (2015): 625–29. http://dx.doi.org/10.14299/ijser.2015.11.005.

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Chatterjee, Ayan, Gourab Dolui, and Dr Uttam Kumar Roy. "Text Based Steganography - A Theoritical Proposal of Text Based Hiding Strategy." International Journal of Scientific and Engineering Research 6, no. 11 (2015): 625–29. http://dx.doi.org/10.14299/ijser.2015.11.010.

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Dissertations / Theses on the topic "Text-based"

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SOARES, FABIO DE AZEVEDO. "AUTOMATIC TEXT CATEGORIZATION BASED ON TEXT MINING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2013. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=23213@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>A Categorização de Documentos, uma das tarefas desempenhadas em Mineração de Textos, pode ser descrita como a obtenção de uma função que seja capaz de atribuir a um documento uma categoria a que ele pertença. O principal objetivo de se construir uma taxonomia de documentos é tornar mais fácil a obtenção de informação relevante. Porém, a implementação e a execução de um processo de Categorização de Documentos não é uma tarefa trivial: as ferramentas de Mineração de Textos estão em processo de amadurecimento e ainda, demandam elevado conhecimento técnico para a sua utilização. Além disso, exercendo grande importância em um processo de Mineração de Textos, a linguagem em que os documentos se encontram escritas deve ser tratada com as particularidades do idioma. Contudo há grande carência de ferramentas que forneçam tratamento adequado ao Português do Brasil. Dessa forma, os objetivos principais deste trabalho são pesquisar, propor, implementar e avaliar um framework de Mineração de Textos para a Categorização Automática de Documentos, capaz de auxiliar a execução do processo de descoberta de conhecimento e que ofereça processamento linguístico para o Português do Brasil.<br>Text Categorization, one of the tasks performed in Text Mining, can be described as the achievement of a function that is able to assign a document to the category, previously defined, to which it belongs. The main goal of building a taxonomy of documents is to make easier obtaining relevant information. However, the implementation and execution of Text Categorization is not a trivial task: Text Mining tools are under development and still require high technical expertise to be handled, also having great significance in a Text Mining process, the language of the documents should be treated with the peculiarities of each idiom. Yet there is great need for tools that provide proper handling to Portuguese of Brazil. Thus, the main aims of this work are to research, propose, implement and evaluate a Text Mining Framework for Automatic Text Categorization, capable of assisting the execution of knowledge discovery process and provides language processing for Brazilian Portuguese.
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NUNES, IAN MONTEIRO. "CLUSTERING TEXT STRUCTURED DATA BASED ON TEXT SIMILARITY." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=25796@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>O presente trabalho apresenta os resultados que obtivemos com a aplicação de grande número de modelos e algoritmos em um determinado conjunto de experimentos de agrupamento de texto. O objetivo de tais testes é determinar quais são as melhores abordagens para processar as grandes massas de informação geradas pelas crescentes demandas de data quality em diversos setores da economia. O processo de deduplicação foi acelerado pela divisão dos conjuntos de dados em subconjuntos de itens similares. No melhor cenário possível, cada subconjunto tem em si todas as ocorrências duplicadas de cada registro, o que leva o nível de erro na formação de cada grupo a zero. Todavia, foi determinada uma taxa de tolerância intrínseca de 5 porcento após o agrupamento. Os experimentos mostram que o tempo de processamento é significativamente menor e a taxa de acerto é de até 98,92 porcento. A melhor relação entre acurácia e desempenho é obtida pela aplicação do algoritmo K-Means com um modelo baseado em trigramas.<br>This document reports our findings on a set of text clusterig experiments, where a wide variety of models and algorithms were applied. The objective of these experiments is to investigate which are the most feasible strategies to process large amounts of information in face of the growing demands on data quality in many fields. The process of deduplication was accelerated through the division of the data set into individual subsets of similar items. In the best case scenario, each subset must contain all duplicates of each produced register, mitigating to zero the cluster s errors. It is established, although, a tolerance of 5 percent after the clustering process. The experiments show that the processing time is significantly lower, showing a 98,92 percent precision. The best accuracy/performance relation is achieved with the K-Means Algorithm using a trigram based model.
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Biedert, Ralf [Verfasser]. "Gaze-Based Human-Text Interaction/Text 2.0 / Ralf Biedert." München : Verlag Dr. Hut, 2014. http://d-nb.info/1050331605/34.

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Prabowo, Rudy. "Ontology-based automatic text classification." Thesis, University of Wolverhampton, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418665.

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This research investigates to what extent ontologies can be used to achieve an accurate classification performance of an automatic text classifier, called the Automatic Classification Engine (ACE). The task of the classifier is to classify Web pages with respect to the Dewey Decimal Classification (DOC) and Library of Congress Classification (LCC) schemes. In particular, this research focuses on how to 1. build a set of ontologies which can provide a mechanism to enable machine reasoning; 2. define the mappings between the ontologies and the two classification schemes; 3. implement an ontology-based classifier. The design and implementation of the classifier concentrates on developing an ontologybased classification model. Given a Web page, the classifier applies the model to carry out reasoning to determine terms - from within the Web page - which represent significant concepts. The classifier, then, uses the mappings to determine the associated DOC and LCC classes of the significant concepts, and assigns the DOC and LCC classes to the Web page. The research also investigates a number of approaches which can be applied to extend the coverage of the ontologies used in a semi-automatic way, since manually constructing ontologies is time consuming. The investigation leads to the design and implementation of a semi-automatic ontology construction system which can recognise new potential terms. By using an ontology editor, those new terms can be integrated into their associated ontologies. An experiment was conducted to validate the effectiveness of the classification model, in which the classifier classified a set of collections of Web pages. The performance of the classifier was measured, in terms of its coverage and accuracy. The experimental evidence shows that the ontology-based automatic text classification approach achieved a better level of performance over the existing approaches.
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Lu, Su. "DCT coefficient based text detection." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 57 p, 2008. http://proquest.umi.com/pqdweb?did=1605147371&sid=4&Fmt=2&clientId=8331&RQT=309&VName=PQD.

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Couairon, Guillaume. "Text-Based Semantic Image Editing." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS248.

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L’objectif de cette thèse est de proposer des algorithmes pour la tâche d’édition d’images basée sur le texte (TIE), qui consiste à éditer des images numériques selon une instruction formulée en langage naturel. Par exemple, étant donné une image d’un chien et la requête "Changez le chien en un chat", nous voulons produire une nouvelle image où le chien a été remplacé par un chat, en gardant tous les autres aspects de l’image inchangés (couleur et pose de l’animal, arrière- plan). L’objectif de l’étoile du nord est de permettre à tout un chacun de modifier ses images en utilisant uniquement des requêtes en langage naturel. Une des spécificités de l’édition d’images basée sur du texte est qu’il n’y a pratiquement pas de données d’entraînement pour former un algorithme supervisé. Dans cette thèse, nous proposons différentes solutions pour l’édition d’images, basées sur l’adaptation de grands modèles multimodaux entraînés sur d’énormes ensembles de données. Nous étudions tout d’abord une configuration d’édition simplifiée, appelée édition d’image basée sur la recherche, qui ne nécessite pas de modifier directement l’image d’entrée. Au lieu de cela, étant donné l’image et la requête de modification, nous recherchons dans une grande base de données une image qui correspond à la modification demandée. Nous nous appuyons sur des modèles multimodaux d’alignement image/texte entraînés sur des ensembles de données à l’échelle du web (comme CLIP) pour effectuer de telles transformations sans aucun exemple. Nous proposons également le cadre SIMAT pour évaluer l’édition d’images basée sur la recherche. Nous étudions ensuite comment modifier directement l’image d’entrée. Nous proposons FlexIT, une méthode qui modifie itérativement l’image d’entrée jus- qu’à ce qu’elle satisfasse un "objectif d’édition" abstrait défini dans un espace d’intégration multimodal. Nous introduisons des termes de régularisation pour imposer des transformations réalistes. Ensuite, nous nous concentrons sur les modèles de diffusion, qui sont des modèles génératifs puissants capables de synthétiser de nouvelles images conditionnées par une grande variété d’invites textuelles. Nous démontrons leur polyvalence en proposant DiffEdit, un algorithme qui adapte les modèles de diffusion pour l’édition d’images sans réglage fin. Nous proposons une stratégie "zero-shot" pour trouver automatiquement où l’image initiale doit être modifiée pour satisfaire la requête de transformation de texte<br>The aim of this thesis is to propose algorithms for the task of Text-based Image Editing (TIE), which consists in editing digital images according to an instruction formulated in natural language. For instance, given an image of a dog, and the query "Change the dog into a cat", we want to produce a novel image where the dog has been replaced by a cat, keeping all other image aspects unchanged (animal color and pose, background). The north-star goal is to enable anyone to edit their images using only queries in natural language. One specificity of text-based image editing is that there is practically no training data to train a supervised algorithm. In this thesis, we propose different solutions for editing images, based on the adaptation of large multimodal models trained on huge datasets. We first study a simplified editing setup, named Retrieval-based image edit- ing, which does not require to directly modify the input image. Instead, given the image and modification query, we search in a large database an image that corresponds to the requested edit. We leverage multimodal image/text alignment models trained on web-scale datasets (like CLIP) to perform such transformations without any examples. We also propose the SIMAT framework for evaluating retrieval-based image editing. We then study how to directly modify the input image. We propose FlexIT, a method which iteratively changes the input image until it satisfies an abstract "editing objective" defined in a multimodal embedding space. We introduce a variety of regularization terms to enforce realistic transformations. Next, we focus on diffusion models, which are powerful generative models able to synthetize novel images conditioned on a wide variety of textual prompts. We demonstrate their versatility by proposing DiffEdit, an algorithm which adapts diffusion models for image editing without finetuning. We propose a zero-shot strategy for finding automatically where the initial image should be changed to satisfy the text transformation query. Finally, we study a specific challenge useful in the context of image editing: how to synthetize a novel image by giving as constraint a spatial layout of objects with textual descriptions, a task which is known as Semantic Image Synthesis. We adopt the same strategy, consisting in adapting diffusion models to solve the task without any example. We propose the ZestGuide algorithm, which leverages the spatio-semantic information encoded in the attention layers of diffusion models
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Gottlieb, Michael. "Text based methods for variant prioritization." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/60358.

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Despite improvements in sequencing technologies, DNA sequence variant interpretation for rare genetic diseases remains challenging. In a typical workflow for the Treatable Intellectual Disability Endeavor in B.C. (TIDE BC), a geneticist examines variant calls to establish a set of candidate variants that explain a patient's phenotype. Even with a sophisticated computation pipeline for variant prioritization, they may need to consider hundreds of variants. This typically involves literature searches on individual variants to determine how well they explain the reported phenotype, which is a time consuming process. In this work, text analysis based variant prioritization methods are developed and assessed for the capacity to distinguish causal variants within exome analysis results for a reference set of individuals with metabolic disorders.<br>Science, Faculty of<br>Graduate
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Zhang, Xuan. "Hardware-based text-to-braille translation." Thesis, Curtin University, 2007. http://hdl.handle.net/20.500.11937/1351.

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Braille, as a special written method of communication for the blind, has been globally accepted for years. It gives blind people another chance to learn and communicate more efficiently with the rest of the world. It also makes possible the translation of printed languages into a written language which is recognisable for blind people. Recently, Braille is experiencing a decreasing popularity due to the use of alternative technologies, like speech synthesis. However, as a form of literacy, Braille is still playing a significant role in the education of people with visual impairments. With the development of electronic technology, Braille turned out to be well suited to computer-aided production because of its coded forms. Software based text-to-Braille translation has been proved to be a successful solution in Assistive Technology (AT). However, the feasibility and advantages of the algorithm reconfiguration based on hardware implementation have rarely been substantially discussed. A hardware-based translation system with algorithm reconfiguration is able to supply greater throughput than a software-based system. Further, it is also expected as a single component integrated in a multi-functional Braille system on a chip.Therefore, this thesis presents the development of a system for text-to-Braille translation implemented in hardware. Differing from most commercial methods, this translator is able to carry out the translation in hardware instead of using software. To find a particular translation algorithm which is suitable for a hardware-based solution, the history of, and previous contributions to Braille translation are introduced and discussed. It is concluded that Markov systems, a formal language theory, were highly suitable for application to hardware based Braille translation. Furthermore, the text-to-Braille algorithm is reconfigured to achieve parallel processing to accelerate the translation speed. Characteristics and advantages of Field Programmable Gate Arrays (FPGAs), and application of Very High Speed Integrated Circuit Hardware Description Language (VHDL) are introduced to explain how the translating algorithm can be transformed to hardware. Using a Xilinx hardware development platform, the algorithm for text-to-Braille translation is implemented and the structure of the translator is described hierarchically.
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Zhang, Xuan. "Hardware-based text-to-braille translation." Curtin University of Technology, Department of Computer Engineering, 2007. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=17220.

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Braille, as a special written method of communication for the blind, has been globally accepted for years. It gives blind people another chance to learn and communicate more efficiently with the rest of the world. It also makes possible the translation of printed languages into a written language which is recognisable for blind people. Recently, Braille is experiencing a decreasing popularity due to the use of alternative technologies, like speech synthesis. However, as a form of literacy, Braille is still playing a significant role in the education of people with visual impairments. With the development of electronic technology, Braille turned out to be well suited to computer-aided production because of its coded forms. Software based text-to-Braille translation has been proved to be a successful solution in Assistive Technology (AT). However, the feasibility and advantages of the algorithm reconfiguration based on hardware implementation have rarely been substantially discussed. A hardware-based translation system with algorithm reconfiguration is able to supply greater throughput than a software-based system. Further, it is also expected as a single component integrated in a multi-functional Braille system on a chip.<br>Therefore, this thesis presents the development of a system for text-to-Braille translation implemented in hardware. Differing from most commercial methods, this translator is able to carry out the translation in hardware instead of using software. To find a particular translation algorithm which is suitable for a hardware-based solution, the history of, and previous contributions to Braille translation are introduced and discussed. It is concluded that Markov systems, a formal language theory, were highly suitable for application to hardware based Braille translation. Furthermore, the text-to-Braille algorithm is reconfigured to achieve parallel processing to accelerate the translation speed. Characteristics and advantages of Field Programmable Gate Arrays (FPGAs), and application of Very High Speed Integrated Circuit Hardware Description Language (VHDL) are introduced to explain how the translating algorithm can be transformed to hardware. Using a Xilinx hardware development platform, the algorithm for text-to-Braille translation is implemented and the structure of the translator is described hierarchically.
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Liljeström, Monica. "Learning text talk online : Collaborative learning in asynchronous text based discussion forums." Doctoral thesis, Umeå universitet, Pedagogiska institutionen, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-34199.

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The desire to translate constructivist and sociocultural approaches to learning in specific learning activities is evident in most forms of training at current, not least in online education. Teachers worldwide are struggling with questions of how to create conditions in this fairly new realm of education for learners to contribute to the development of a good quality in their own and others' learning. Collaboration in forms of text talk in asynchronous, text based forums (ADF) is often used so students can participate at the location and time that suits them best given the other aspects of their life situation. But previous research show how collaboration in forms of text talk do not always evolve in expected quality, and how participation sometimes can be so low that no discussions at all take place. Perhaps it is time to move on and make use of the variety of user-friendly audio-visible technologies that offers conditions for collaboration similar to those in the physical environment? Is there any point to use ADF for collaboration, beyond the flexible opportunity for participation it allows? If so, why, how and under what conditions are it worthwhile to use ADF for tasks meant to be worked collaboratively on? These questions were the starting point of the studies in this thesis that was researched through two case studies involving different techniques and data samples of various natures, with the aim to understand more about collaborative text talk. The research approach differs from the vast majority of studies in the research field of Computer Supported Collaborative Learning (CSCL) where many studies currently are conducted by analysis of quantifiable data. The first case study was conducted in the context of non-formal learning in Swedish Liberal Adult Education online, and the second in the context of higher education online in Sweden. The studies in the thesis were made on basis of socio-cultural theory and empirical studies. Empirical data was collected from questionnaires, interviews and texts created by students participating in tasks that they jointly resolved through text talk. Some results were brought back to the students for further explanation of the results. Findings from data analysis were triangulated with other results and with sociocultural theory. The results indicate that students can create knowledge relevant to their studies through text talk, but can feel restrained or dismiss the activity as irrelevant if important conditions are lacking.  Collaboration through text talk makes individual resources accessible in a specific place where it can be observed and its validity for the purpose of the task evaluated by others. Students with good insight in what they are supposed to accomplish seem be able to consult relevant guidance for this evaluation, from teachers, textbooks, scientific articles and other valid experiences important to their studies, and thereby contribute to learning of the quality they studies are meant to produce. Text talk also increases teachers’ possibilities to identify what the guidance the study group needs when evaluating the gathered resources and through their own active participation provide support in the students “zone of proximal development”. Contributions offered to the CSCL research field is the identifications of important mechanisms related to learning collaboratively through text talk, and the use of case study methodology as inspiration for others to try also these kinds of strategies to capture online learning.
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Books on the topic "Text-based"

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Mickan, Peter, and Elise Lopez, eds. Text-Based Research and Teaching. Palgrave Macmillan UK, 2017. http://dx.doi.org/10.1057/978-1-137-59849-3.

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Sharpe, Pamela J. TOEFL iBT: Internet -based text. Barron's Educational Series, Inc, 2010.

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Zuberec, Sarah Elizabeth. Visualization of text based information. National Library of Canada, 1993.

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D, Hanser Robert, ed. Community-based corrections: A text/reader. SAGE, 2012.

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Great Britain. Treasury. Central Computer and Telecommunications Agency., ed. Text-based information management systems volume. H.M.S.O., 1991.

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Central Computer and Telecommunications Agency., ed. Text-based information management systems volume. HMSO, 1991.

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Sullivan, Palincsar Annemarie, ed. Comprehension instruction through text-based discussion. International Reading Association, 2013.

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Neustein, Amy. Text mining of web-based medical content. Boston, 2014.

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W, Jewett John, ed. Principles of physics: A calculus-based text. 4th ed. Brooks/Cole, 2006.

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W, Jewett John, ed. Principles of physics: A calculus-based text. 4th ed. Thomson-Brooks/Cole, 2006.

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Book chapters on the topic "Text-based"

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Aspin, Adam. "Text-Based Visualizations." In Pro Power BI Desktop. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1805-1_10.

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Person, Ron. "Text-Based Dashboards." In Balanced Scorecards & Operational Dashboards with Microsoft® Excel®. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118984000.ch15.

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Tyers, Ben. "Text-Based Quiz." In GameMaker: Studio 100 Programming Challenges. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2644-5_33.

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Wu, Qi, Peng Wang, Xin Wang, Xiaodong He, and Wenwu Zhu. "Text-Based VQA." In Visual Question Answering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0964-1_12.

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Baqach, Adil, and Amal Battou. "Text-Based Sentiment Analysis." In International Conference on Advanced Intelligent Systems for Sustainable Development. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26384-2_10.

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Whitehurst, Roy S. "Evaluate Text-Based Arguments." In Teaching Media Literacy with Social Media News. Routledge, 2024. http://dx.doi.org/10.4324/9781003471301-18.

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Terzi, Maria, Matthew Rowe, Maria-Angela Ferrario, and Jon Whittle. "Text-Based User-kNN: Measuring User Similarity Based on Text Reviews." In User Modeling, Adaptation, and Personalization. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08786-3_17.

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"Text-based interpretation." In Modern Statutory Interpretation. Cambridge University Press, 2022. http://dx.doi.org/10.1017/9781108895637.012.

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Brokaw, Katherine Steele. "Text-based / concept-driven." In Shakespeare / Text. The Arden Shakespeare, 2021. http://dx.doi.org/10.5040/9781350128170.ch-12.

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"Why Text-Based Questions?" In Teaching With Text-Based Questions. Routledge, 2014. http://dx.doi.org/10.4324/9781315813219-8.

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Conference papers on the topic "Text-based"

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Zhang, Yupeng, Jiong Hang Wu, Zhengkui Chen, Hai Huang, and Guide Zhu. "Text-based Visual Question Answering Based on Text-Aware Pre-Training." In 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML). IEEE, 2024. https://doi.org/10.1109/icicml63543.2024.10957919.

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Canelón, Rodolfo, Francisco Cruz, and Irisysleyer Barrios. "A text mining-based method for domains using AI generative text-to-text." In 2024 IEEE Latin American Conference on Computational Intelligence (LA-CCI). IEEE, 2024. https://doi.org/10.1109/la-cci62337.2024.10814840.

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Priyanka, S., S. Santhiya, S. Keerthika, B. Divyadharshini, P. Madan, and Ms Yaswin. "Personality Based Text Generation." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724424.

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Lin, Xingtao, Chuanyang Gong, Lanxiao Wang, Heqian Qiu, Shengyu Tong, and Hongliang Li. "A Text Detector Based on the Specific Text Prompt." In 2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10647439.

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Liu, Zhiyuan, An Zhang, Hao Fei, et al. "ProtT3: Protein-to-Text Generation for Text-based Protein Understanding." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.324.

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Zhang, Jisi, Pablo Peso Parada, Md Asif Jalal, and Karthikeyan Saravanan. "Diffusion based Text-to-Music Generation with Global and Local Text based Conditioning." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889289.

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Malani, Pranav, and Prafulla Bafna. "Context-based Marwari text clustering." In 2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES). IEEE, 2024. https://doi.org/10.1109/spices62143.2024.10779675.

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Shu, Xin, Xinping Long, and Ying Ma. "BiGRU Based Text Intent Recognition." In 2024 6th International Workshop on Artificial Intelligence and Education (WAIE). IEEE, 2024. https://doi.org/10.1109/waie63876.2024.00068.

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Uppala, Sowmya, Saradhambica Kesavarapu, NV S. Shankar, P. A. Sunny Dayal, A. Sravani, and CVKK Chaitanya. "Web Based Legal Text Summarizer." In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 2024. https://doi.org/10.1109/scopes64467.2024.10991261.

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Xie, Binqing, and Gady Agam. "Boosting based text and non-text region classification." In IS&T/SPIE Electronic Imaging, edited by Gady Agam and Christian Viard-Gaudin. SPIE, 2011. http://dx.doi.org/10.1117/12.876736.

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Reports on the topic "Text-based"

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Simon, Benjamin. Evaluating Cloud-Based Speech-to-Text Services. Iowa State University, 2021. http://dx.doi.org/10.31274/cc-20240624-641.

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Hoberg, Gerard, and Gordon Phillips. Text-Based Network Industries and Endogenous Product Differentiation. National Bureau of Economic Research, 2010. http://dx.doi.org/10.3386/w15991.

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Braun, Ronald. Ontology-Based Information Extraction from Free-Form Text. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada383044.

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Carlson, Lynn, Elizabeth Cooper, Ronald Dolan, and Steve J. Maiorano. Representing Text Meaning for Multilingual Knowledge-Based Machine Translation. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada302333.

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Calomiris, Charles, Harry Mamaysky, and Ruoke Yang. Measuring the Cost of Regulation: A Text-Based Approach. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w26856.

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Paynter, Julie, Ian McCulloh, and John Graham. Application of Confidence Intervals to Text-Based Social Network Construction. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada488539.

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Dasigi, V. R., and R. C. Mann. Toward a multi-sensor-based approach to automatic text classification. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/130610.

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Han, Xuehua, Juanle Wang, and Yuelei Yuan. Extraction and Analysis of Earthquake Events Information based on Web Text. International Science Council, 2019. http://dx.doi.org/10.24948/2019.06.

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Brill, Eric. Automatic Grammar Induction and Parsing Free Text: A Transformation-Based Approach. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada458695.

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Page, Lindsay, Katharine Meyer, Jeonghyun Lee, and Hunter Gehlbach. Conditions Under Which College Students Can be Responsive to Text-based Nudging. National Bureau of Economic Research, 2024. https://doi.org/10.3386/w33257.

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