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: 1 February 2022

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Journal articles on the topic "Text-based"

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D., Mhamdi. "Job Recommendation System based on Text Analysis." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 1025–30. http://dx.doi.org/10.5373/jardcs/v12sp4/20201575.

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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 (November 13, 2009): 2348–50. http://dx.doi.org/10.3724/sp.j.1087.2009.02348.

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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 (May 31, 2016): 209–20. http://dx.doi.org/10.14257/ijmue.2016.11.5.19.

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More, Prof Vijay, Ms Ankita Shetty, and Ms Aishwarya Mapara Mr Rahul Ghuge Mr Rohit Sharma. "Employee Data Mining Based on Text and Image Processing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 379–81. http://dx.doi.org/10.31142/ijtsrd10791.

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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 (November 25, 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 (November 25, 2015): 625–29. http://dx.doi.org/10.14299/ijser.2015.11.010.

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S. J, Rexline, Robert L, and Trujilla Lobo.F. "Dictionary Based Text Filter for Lossless Text Compression." International Journal of Computer Trends and Technology 49, no. 3 (July 25, 2017): 143–49. http://dx.doi.org/10.14445/22312803/ijctt-v49p122.

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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
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
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.
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
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
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.
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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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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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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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.
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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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.
Science, Faculty of
Graduate
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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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Davis, Marcia H. "Effects of text markers and familiarity on component structures of text-based representations." College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/4086.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2006.
Thesis research directed by: Human Development. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Zhang, Nan. "TRANSFORM BASED AND SEARCH AWARE TEXT COMPRESSION SCHEMES AND COMPRESSED DOMAIN TEXT RETRIEVAL." Doctoral diss., University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3938.

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In recent times, we have witnessed an unprecedented growth of textual information via the Internet, digital libraries and archival text in many applications. While a good fraction of this information is of transient interest, useful information of archival value will continue to accumulate. We need ways to manage, organize and transport this data from one point to the other on data communications links with limited bandwidth. We must also have means to speedily find the information we need from this huge mass of data. Sometimes, a single site may also contain large collections of data such as a library database, thereby requiring an efficient search mechanism even to search within the local data. To facilitate the information retrieval, an emerging ad hoc standard for uncompressed text is XML which preprocesses the text by putting additional user defined metadata such as DTD or hyperlinks to enable searching with better efficiency and effectiveness. This increases the file size considerably, underscoring the importance of applying text compression. On account of efficiency (in terms of both space and time), there is a need to keep the data in compressed form for as much as possible. Text compression is concerned with techniques for representing the digital text data in alternate representations that takes less space. Not only does it help conserve the storage space for archival and online data, it also helps system performance by requiring less number of secondary storage (disk or CD Rom) accesses and improves the network transmission bandwidth utilization by reducing the transmission time. Unlike static images or video, there is no international standard for text compression, although compressed formats like .zip, .gz, .Z files are increasingly being used. In general, data compression methods are classified as lossless or lossy. Lossless compression allows the original data to be recovered exactly. Although used primarily for text data, lossless compression algorithms are useful in special classes of images such as medical imaging, finger print data, astronomical images and data bases containing mostly vital numerical data, tables and text information. Many lossy algorithms use lossless methods at the final stage of the encoding stage underscoring the importance of lossless methods for both lossy and lossless compression applications. In order to be able to effectively utilize the full potential of compression techniques for the future retrieval systems, we need efficient information retrieval in the compressed domain. This means that techniques must be developed to search the compressed text without decompression or only with partial decompression independent of whether the search is done on the text or on some inversion table corresponding to a set of key words for the text. In this dissertation, we make the following contributions: (1) Star family compression algorithms: We have proposed an approach to develop a reversible transformation that can be applied to a source text that improves existing algorithm's ability to compress. We use a static dictionary to convert the English words into predefined symbol sequences. These transformed sequences create additional context information that is superior to the original text. Thus we achieve some compression at the preprocessing stage. We have a series of transforms which improve the performance. Star transform requires a static dictionary for a certain size. To avoid the considerable complexity of conversion, we employ the ternary tree data structure that efficiently converts the words in the text to the words in the star dictionary in linear time. (2) Exact and approximate pattern matching in Burrows-Wheeler transformed (BWT) files: We proposed a method to extract the useful context information in linear time from the BWT transformed text. The auxiliary arrays obtained from BWT inverse transform brings logarithm search time. Meanwhile, approximate pattern matching can be performed based on the results of exact pattern matching to extract the possible candidate for the approximate pattern matching. Then fast verifying algorithm can be applied to those candidates which could be just small parts of the original text. We present algorithms for both k-mismatch and k-approximate pattern matching in BWT compressed text. A typical compression system based on BWT has Move-to-Front and Huffman coding stages after the transformation. We propose a novel approach to replace the Move-to-Front stage in order to extend compressed domain search capability all the way to the entropy coding stage. A modification to the Move-to-Front makes it possible to randomly access any part of the compressed text without referring to the part before the access point. (3) Modified LZW algorithm that allows random access and partial decoding for the compressed text retrieval: Although many compression algorithms provide good compression ratio and/or time complexity, LZW is the first one studied for the compressed pattern matching because of its simplicity and efficiency. Modifications on LZW algorithm provide the extra advantage for fast random access and partial decoding ability that is especially useful for text retrieval systems. Based on this algorithm, we can provide a dynamic hierarchical semantic structure for the text, so that the text search can be performed on the expected level of granularity. For example, user can choose to retrieve a single line, a paragraph, or a file, etc. that contains the keywords. More importantly, we will show that parallel encoding and decoding algorithm is trivial with the modified LZW. Both encoding and decoding can be performed with multiple processors easily and encoding and decoding process are independent with respect to the number of processors.
Ph.D.
School of Computer Science
Engineering and Computer Science
Computer Science
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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. London: 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. Hauppauge, N.Y: Barron's Educational Series, Inc, 2010.

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

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

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

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Katharina, Von Hammerstein, ed. Interaktion: A text-based intermediate German course. Boston: Houghton Mifflin Co., 1990.

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

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

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W, Jewett John, ed. Principles of physics: A calculus-based text. 3rd ed. [Pacific Grove, CA]: Brooks/Cole, 2002.

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Dawson, Michael. C++ projects: Programming with text-based games. Boston, Mass: Cengage Course Technology, 2009.

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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, 301–32. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1805-1_10.

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Tyers, Ben. "Text-Based Quiz." In GameMaker: Studio 100 Programming Challenges, 65–66. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2644-5_33.

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Person, Ron. "Text-Based Dashboards." In Balanced Scorecards & Operational Dashboards with Microsoft® Excel®, 167–81. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118984000.ch15.

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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, 195–206. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08786-3_17.

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Rothkrantz, Léon J. M., and Ania Wojdel. "A Text Based Talking Face." In Text, Speech and Dialogue, 327–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45323-7_55.

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Charles, Lauren E., William Smith, Jeremiah Rounds, and Joshua Mendoza. "Text-Based Analytics for Biosurveillance." In Advanced Data Analytics in Health, 117–31. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77911-9_7.

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Marton, Yuval, Ning Wu, and Lisa Hellerstein. "On Compression-Based Text Classification." In Lecture Notes in Computer Science, 300–314. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31865-1_22.

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Song, Shaoxu, Jian Zhang, and Chunping Li. "Concept Chain Based Text Clustering." In Computational Intelligence and Security, 713–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596448_105.

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Liu, Yaqi, and Zhijiang Li. "Semantic Based Text Similarity Computation." In Lecture Notes in Electrical Engineering, 343–48. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3530-2_43.

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Leon, Miriam, Veronica Vilaplana, Antoni Gasull, and Ferran Marques. "Region-Based Caption Text Extraction." In Lecture Notes in Electrical Engineering, 21–36. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3831-1_2.

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Conference papers on the topic "Text-based"

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Dalal, M. K., and M. A. Zaveri. "Heuristics based automatic text summarization of unstructured text." In the International Conference & Workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1980022.1980170.

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Huang, Xiaodong, Qin Wang, Lishang Zhu, and Kehua Liu. "Video text detection based on text edge map." In 2013 3rd International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2013. http://dx.doi.org/10.1109/iccsnt.2013.6967273.

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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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Weintrop, David. "Comparing Text-based, Blocks-based, and Hybrid Blocks/Text Programming Tools." In ICER '15: International Computing Education Research Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2787622.2787752.

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Koleejan, Chahine, and Xiaoying Gao. "View-based text representation." In 2016 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2016. http://dx.doi.org/10.1109/cec.2016.7743804.

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Chiru, Costin-Gabriel, and Asmelash Teka Hadgu. "Sentiment-based text segmentation." In 2013 2nd International Conference on Systems and Computer Science (ICSCS). IEEE, 2013. http://dx.doi.org/10.1109/icconscs.2013.6632053.

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Nandi, Biswarup, Mousumi Ghanti, and Souvik Paul. "Text based sentiment analysis." In 2017 International Conference on Inventive Computing and Informatics (ICICI). IEEE, 2017. http://dx.doi.org/10.1109/icici.2017.8365326.

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Iwayama, Makoto, and Takenobu Tokunaga. "Cluster-based text categorization." In the 18th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 1995. http://dx.doi.org/10.1145/215206.215371.

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Binwahlan, Mohammed Salem, Naomie Salim, and Ladda Suanmali. "Swarm Based Text Summarization." In 2009 International Association of Computer Science and Information Technology - Spring Conference. IEEE, 2009. http://dx.doi.org/10.1109/iacsit-sc.2009.61.

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Hassan, Ehtesham, Ritu Garg, Santanu Chaudhury, and M. Gopal. "Script based text identification." In the 2011 Joint Workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2034617.2034630.

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Reports on the topic "Text-based"

1

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

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

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Carlson, Lynn, Elizabeth Cooper, Ronald Dolan, and Steve J. Maiorano. Representing Text Meaning for Multilingual Knowledge-Based Machine Translation. Fort Belvoir, VA: Defense Technical Information Center, October 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. Cambridge, MA: National Bureau of Economic Research, March 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. Fort Belvoir, VA: Defense Technical Information Center, January 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), October 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. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada458695.

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Hoberg, Gerard, and Gordon Phillips. Product Market Synergies and Competition in Mergers and Acquisitions: A Text-Based Analysis. Cambridge, MA: National Bureau of Economic Research, August 2008. http://dx.doi.org/10.3386/w14289.

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Zhao, Li, Rafiqul Islam Rana, and Muzhen Li. A Study of Sustainability Practices of US Fashion Brands Through Dictionary-Based Text Analysis. Ames (Iowa): Iowa State University. Library, January 2019. http://dx.doi.org/10.31274/itaa.8441.

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