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Artículos de revistas sobre el tema "Text analysis"

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Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de mayo de 2024

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1

Z.K., Orazimbetova y Mukhiyatdinova T. "LINGUISTIC ANALYSIS OF NEWSPAPER TEXT". CURRENT RESEARCH JOURNAL OF PHILOLOGICAL SCIENCES 02, n.º 10 (1 de octubre de 2021): 82–85. http://dx.doi.org/10.37547/philological-crjps-02-10-16.

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2

Neha K, Shah. "Introduction of Text mining and an Analysis of Text mining Techniques". Paripex - Indian Journal Of Research 2, n.º 2 (15 de enero de 2012): 56–57. http://dx.doi.org/10.15373/22501991/feb2013/18.

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3

V., Dr Sellam. "Text Analysis Via Composite Feature Extraction". Journal of Advanced Research in Dynamical and Control Systems 24, n.º 4 (31 de marzo de 2020): 310–20. http://dx.doi.org/10.5373/jardcs/v12i4/20201445.

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4

H., D. P. "Text analysis". Nature 356, n.º 6372 (abril de 1992): 740. http://dx.doi.org/10.1038/356740a0.

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5

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

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6

Khan, Nida Zafar y Prof S. R. Yadav. "Analysis of Text Classification Algorithms: A Review". International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (28 de febrero de 2019): 579–81. http://dx.doi.org/10.31142/ijtsrd21448.

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7

Adewumi, Sunday Eric. "Character Analysis Scheme for Compressing Text Files". International Journal of Computer Theory and Engineering 7, n.º 5 (octubre de 2015): 362–65. http://dx.doi.org/10.7763/ijcte.2015.v7.986.

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8

Makhmudovna, Madirimova Sokhiba, Abdulhaq Rahimi Saripul y Jamila Eisar. "ANALYSIS OF TEXT DIFFERENCES IN MUTRIB'S WORKS". American Journal Of Philological Sciences 03, n.º 04 (1 de abril de 2023): 41–47. http://dx.doi.org/10.37547/ajps/volume03issue04-07.

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9

Ding, Yong, Yongsheng Han, Guozhen Lu y Xinfeng Wu. "Boundedness of Singular Integrals on Multiparameter Weighted Hardy Spaces $\text{{\textit{H}}}^\text{{\textit{p}}}_{\text{{\textit{w}}}}\ (\mathbb{R}^{\text{{\textit{n}}}}\times \mathbb{R}^{\text{{\textit{m}}}})$". Potential Analysis 37, n.º 1 (9 de agosto de 2011): 31–56. http://dx.doi.org/10.1007/s11118-011-9244-y.

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10

Fréchet, Nadjim, Justin Savoie y Yannick Dufresne. "Analysis of Text-Analysis Syllabi: Building a Text-Analysis Syllabus Using Scaling". PS: Political Science & Politics 53, n.º 2 (29 de noviembre de 2019): 338–43. http://dx.doi.org/10.1017/s1049096519001732.

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ABSTRACTIn the last decade, text-analytic methods have become a fundamental element of a political researcher’s toolkit. Today, text analysis is taught in most major universities; many have entire courses dedicated to the topic. This article offers a systematic review of 45 syllabi of text-analysis courses around the world. From these syllabi, we extracted data that allowed us to rank canonical sources and discuss the variety of software used in teaching. Furthermore, we argue that our empirical method for building a text-analysis syllabus could easily be extended to syllabi for other courses. For instance, scholars can use our technique to introduce their graduate students to the field of systematic reviews while improving the quality of their syllabi.

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11

Pietrzyk, P. M. "Free text analysis". International Journal of Bio-Medical Computing 39, n.º 1 (abril de 1995): 139–44. http://dx.doi.org/10.1016/0020-7101(94)01092-f.

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12

Shaw, Mildred L. G. "Practical Text Analysis". Contemporary Psychology: A Journal of Reviews 39, n.º 4 (abril de 1994): 371. http://dx.doi.org/10.1037/034072.

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13

Rana, Shweta. "Sentiment Analysis for Hindi Text using Fuzzy Logic". Indian Journal of Applied Research 4, n.º 8 (1 de octubre de 2011): 437–40. http://dx.doi.org/10.15373/2249555x/august2014/111.

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14

YOSHIMI, TAKEHIKO, JIRI JELINEK, OSAMU NISHIDA, NAOYUKI TAMURA y HARUO MURAKAMI. "Text Analysis based on Text-Wide Grammar". Journal of Natural Language Processing 4, n.º 1 (1997): 3–21. http://dx.doi.org/10.5715/jnlp.4.3.

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15

WYSKWARSKI, Marcin. "Project managers’ competences – text mining analysis". Scientific Papers of Silesian University of Technology. Organization and Management Series 2018, n.º 130 (2018): 673–85. http://dx.doi.org/10.29119/1641-3466.2018.130.58.

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16

Usmanov, Farruh Musoevich. "COGNITIVE APPROACH TO THE ANALYSIS OF THE LITERARY TEXT". American Journal of Philological Sciences 3, n.º 12 (1 de diciembre de 2023): 18–22. http://dx.doi.org/10.37547/ajps/volume03issue12-04.

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The article deals with the cognitive linguistics is the study of language in its cognitive function, where cognitive refers to the crucial role of informational structures in the human mind and language. Cognitive linguistics focuses on the processes of literary text production, interpretation, and evaluation.

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17

Sirojiddin, Pardayev. "THE PRINCIPLES OF CULTURAL ANALYSIS OF THE ARTISTIC TEXT". American Journal of Philological Sciences 4, n.º 1 (1 de enero de 2024): 64–67. http://dx.doi.org/10.37547/ajps/volume04issue01-11.

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In this article, the culturological analysis of the literary text involves the comprehensive analysis of its linguistic essence, revealing the national-cultural characteristics of the author of the artistic work. An attempt was made to reveal four principles of culturological analysis of the work of art. Culture was interpreted from four points of view: elite, village, city and etc. In other words, there are four similar elements related to linguistics in culture: 1) elite ("bookish") culture corresponds to a timeless language; 2) that dialects correspond to the national culture; 3) it was discussed that the intermediate culture ("culture for the people", "third culture") corresponds to the national languages (for example, the subculture of beekeepers, engineers, doctors, teachers, programmers, etc.).

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18

Kluyeva, Ljudmila Borisovna. "Film as Text. Some Aspects of Text Analysis". Journal of Flm Arts and Film Studies 5, n.º 2 (15 de mayo de 2013): 6–16. http://dx.doi.org/10.17816/vgik526-16.

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The article focuses on the problem of the text as a universal category and the primary reality of any studied cultural phenomenon. It also touches upon a range of issues connected with different approaches to literary text analysis.

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19

MENGİ, Mine. "The Grade Of Text Presenting, Terms, Text Analysis". Journal of Turkish Studies Volume 2 Issue 3, n.º 2 (2007): 407–17. http://dx.doi.org/10.7827/turkishstudies.140.

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20

SPROAT, RICHARD. "Multilingual text analysis for text-to-speech synthesis". Natural Language Engineering 2, n.º 4 (diciembre de 1996): 369–80. http://dx.doi.org/10.1017/s1351324997001654.

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We present a model of text analysis for text-to-speech (TTS) synthesis based on (weighted) finite state transducers, which serves as the text analysis module of the multilingual Bell Labs TTS system. The transducers are constructed using a lexical toolkit that allows declarative descriptions of lexicons, morphological rules, numeral-expansion rules, and phonological rules, inter alia. To date, the model has been applied to eight languages: Spanish, Italian, Romanian, French, German, Russian, Mandarin and Japanese.

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21

EEMEREN, FRANS H. "Dialectical analysis as a normative reconstruction of argumentative discourse". Text - Interdisciplinary Journal for the Study of Discourse 6, n.º 1 (1986): 1–16. http://dx.doi.org/10.1515/text.1.1986.6.1.1.

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22

Divekar, Akash. "Analysis on Text Summarization". International Journal for Research in Applied Science and Engineering Technology 10, n.º 6 (30 de junio de 2022): 4222–29. http://dx.doi.org/10.22214/ijraset.2022.44848.

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Abstract: As we enter the 21st century, with the advent of mobile phones and access to information stores, we seem to be surrounded by more information, less time, or the ability to process it. The creation of automated summaries was a clever human solution to this complex problem. However, the application of this solution was very complicated. In fact, there are a number of problems that need to be addressed before the promises of an automated text can be fully realized. Basically, it is necessary to understand how people summarize the text and build a system based on that. However, people are different in their thinking and interpretation that it is difficult to make a "gold standard" summary in which product summaries will be tested. In this paper, we will discuss the basic concepts of this article by providing the most appropriate definitions, characterization, types and two different methods of automatic text abstraction: extraction and extraction. Special attention is given to the method of extraction. It consists of selecting sentences and paragraphs that are important in the original text and combining them into a short form. It is mentally simple and easy to use

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23

Teshabaeva, Dilfuza Muminovna. "PRESS LANGUAGE: TEXT ANALYSIS." Theoretical & Applied Science 92, n.º 12 (30 de diciembre de 2020): 288–91. http://dx.doi.org/10.15863/tas.2020.12.92.56.

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24

Danilova, Anna G. y Olga V. Mitina. "COMPUTERIZED QUALITATIVE TEXT ANALYSIS". Moscow University Psychology Bulletin, n.º 1 (2021): 220–40. http://dx.doi.org/10.11621/vsp.2021.01.09.

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Relevance. In applied psychology, researchers are increasingly faced with the task of analyzing large amounts of text, both verbal and visual. For these purposes, various methods of analysis have been developed and various computer programs have been created. Objective. The possibilities of computerized qualitative text analysis are analyzed. The spectrum of problems solved by various methods of qualitative text analysis is considered. Methods. The theoretical analysis of classical methods of qualitative text analysis is carried out. The specificity of the method of computerized discourse analysis is analyzed. The Internet searched for the most popular programs for qualitative text analysis, considered the main options and requirements for working in the program. Results. Methods of content-, discourse-, intent-analysis, conversion and narrative analysis are briefly characterized. The computerized discourse analysis (CMDA) approach is described. An approach to solving problems of various methods of qualitative analysis of text in computer analysis of qualitative data (Computer Assisted Qualitative Data Analysis; CAQDAS) is presented. The possibilities of some software packages developed within the CAQDAS approach are considered. Conclusions. The advantages of using computerized discourse analysis are shown. The topic of research of methodological problems accompanying the use of CAQDAS is outlined.

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25

Dahroj, Fawaz Ahmed y Raphael Salkie. "Text and Discourse Analysis". Language 73, n.º 3 (septiembre de 1997): 692. http://dx.doi.org/10.2307/415957.

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26

Doyle, Dominic. "Structural Analysis of Text". Teaching Theology & Religion 15, n.º 1 (enero de 2012): 40. http://dx.doi.org/10.1111/j.1467-9647.2011.00758.x.

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27

Welbers, Kasper, Wouter Van Atteveldt y Kenneth Benoit. "Text Analysis in R". Communication Methods and Measures 11, n.º 4 (2 de octubre de 2017): 245–65. http://dx.doi.org/10.1080/19312458.2017.1387238.

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28

Mills, Sara. "Post-feminist text analysis". Language and Literature: International Journal of Stylistics 7, n.º 3 (agosto de 1998): 235–52. http://dx.doi.org/10.1177/096394709800700304.

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This article argues that there is a need for a new form of feminist text analysis which would take account of the changes which have occurred in feminist theory, linguistic theory, critical text analysis and in sexism itself. Rather than relying on relatively simple models of interpretation, this new form of analysis, post-feminist text analysis, would demonstrate awareness of the complexity and context-specific nature of the meanings of words within texts; it would also be aware of the necessity to develop new models of analysis for sexism and gender relations. In the analysis of a British advertisement for a dating agency following this discussion, I have tried to indicate directions in which this form of analysis might take.1

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29

Perkins, Mark. "Approaches to Text Analysis". Global Language Review IV, n.º I (30 de junio de 2019): 1–7. http://dx.doi.org/10.31703/glr.2019(iv-i).01.

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Text Analysis is a broad term that covers many approaches and technologies. Those initially stemming from the academic sphere have come to enter the commercial, and today there is a wide interplay between the two. A further dichotomy is that between natural language and computational approaches. Over time, approaches have come to draw upon each other, although there are still clear dividing lines and practitioners tend to rely mainly on one approach or the other. This paper seeks to draw out these approaches and give an account of them over time. It also points to future developments where artificial intelligence is increasingly used.

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30

Senthilkumar, K. y E. Ruchika Mehra Vijayan. "Real Time Text Analysis". IOP Conference Series: Materials Science and Engineering 263 (noviembre de 2017): 042005. http://dx.doi.org/10.1088/1757-899x/263/4/042005.

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31

Sakr, Mohammed Gamal. "Prosodic text analysis theory". Journal of Arts and Social Sciences [JASS] 6, n.º 2 (1 de enero de 2016): 283. http://dx.doi.org/10.24200/jass.vol7iss1pp283-295.

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Within the field of poetic text linguistics, the theories of both text linguistics and prosodic linguistics have failed to reveal the nature of the creation of the poetic text and the way it is received by people. This necessitates adopting a new theoretical framework that combines the two approaches by focusing on nine norms: domain rules, length and separation rules, paragraph and sentence rules, phrases and words rules, and syllable and sound rules. These rules should be used by anyone who compares between different poetic texts on the one hand, and between poetic and non-poetic texts on the other.

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32

Sakr, Mohammed Gamal. "Prosodic text analysis theory". Journal of Arts and Social Sciences [JASS] 7, n.º 1 (1 de enero de 2016): 283–95. http://dx.doi.org/10.53542/jass.v7i1.1118.

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Within the field of poetic text linguistics, the theories of both text linguistics and prosodic linguistics have failed to reveal the nature of the creation of the poetic text and the way it is received by people. This necessitates adopting a new theoretical framework that combines the two approaches by focusing on nine norms: domain rules, length and separation rules, paragraph and sentence rules, phrases and words rules, and syllable and sound rules. These rules should be used by anyone who compares between different poetic texts on the one hand, and between poetic and non-poetic texts on the other.

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33

Sekhar, Sowmik. "Tokenization for Text Analysis". International Journal of Scientific Research and Engineering Trends 10, n.º 1 (30 de enero de 2024): 149–52. http://dx.doi.org/10.61137/ijsret.vol.10.issue1.127.

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34

Song, Hongli y Yuqian Song. "Knowledge Mapping of Text Linguistics: Discourse Analysis, Text Analysis and Social Media". Scientific and Social Research 3, n.º 6 (29 de diciembre de 2021): 50–64. http://dx.doi.org/10.36922/ssr.v3i6.1278.

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Text linguistics is becoming a significant reference source for theorist as well as practitioners in information fusion, decision making and operations, given its research focus ranging from discourse analysis, text analysis and social media-inspired analysis. This article reviews and maps academic literature in the subject of text linguistics using a cutting-edge scientometrics approach. It recognizes major research outputs, themes, and authors in this way. It compares data harvests to discuss the field’s future trends. The work is the first systematic mapping of the subject of text linguistics, and it has important implications for using the scientometrics method to review academic publications.

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35

Gu, Yueguo. "Multimodal text analysis: A corpus linguistic approach to situated discourse". Text & Talk - An Interdisciplinary Journal of Language, Discourse Communication Studies 26, n.º 2 (20 de enero de 2006): 127–67. http://dx.doi.org/10.1515/text.2006.007.

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36

TYLER, ANDREA. "Discourse structure and specification of relationships: A cross-linguistic analysis". Text - Interdisciplinary Journal for the Study of Discourse 12, n.º 1 (1992): 1–18. http://dx.doi.org/10.1515/text.1.1992.12.1.1.

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37

Newman, Michael. "Rap as literacy: A genre analysis of Hip-Hop ciphers". Text - Interdisciplinary Journal for the Study of Discourse 25, n.º 3 (mayo de 2005): 399–436. http://dx.doi.org/10.1515/text.2005.25.3.399.

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38

O'Halloran, Kieran. "Inferencing and cultural reproduction: a corpus-based critical discourse analysis". Text & Talk - An Interdisciplinary Journal of Language, Discourse Communication Studies 29, n.º 1 (enero de 2009): 21–51. http://dx.doi.org/10.1515/text.2009.002.

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39

Choi, Jinbong. "An analysis of persuasive communication in Korean home shopping advertising". Text & Talk - An Interdisciplinary Journal of Language, Discourse & Communication Studies 31, n.º 6 (enero de 2011): 643–59. http://dx.doi.org/10.1515/text.2011.031.

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40

O., Ocherklevich y Ihnatovych A. "METHODS OF SEMANTIC ANALYSIS IN ANNOTATED GENERALIZATION OF TEXT DOCUMENTS". Computer systems and network 2, n.º 1 (23 de marzo de 2017): 53–58. http://dx.doi.org/10.23939/csn2020.01.053.

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The article is devoted to the use of semantic analysis in the generalization of text documents. The analysis of features of the most widespread methods of generalization of text documents and an estimation of quality of results of an estimation is carried out. Features of the improved method of annotative generalization of text documents, which uses the principles of hidden semantic analysis and elements of fuzzy logic to identify semantically important sentences, are presented. It is proposed to use a new approach to evaluating the effectiveness of generalization, based on elements of fuzzy logic and a statistical indicator used to assess the importance of words in the context and class of the document, which allows to determine the correspondence between the original document and its summary. The results of verification of the proposed tools, certifying their effectiveness. Keywords: text document, annotation generalization, semantic analysis, fuzzy logic, evaluation, efficiency

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41

Jones, Steven. "A lexico-syntactic analysis of antonym co-occurrence in spoken English". Text & Talk - An Interdisciplinary Journal of Language, Discourse Communication Studies 26, n.º 2 (20 de enero de 2006): 191–216. http://dx.doi.org/10.1515/text.2006.009.

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42

Haiyuan Guo, Haiyuan Guo, Xuegang Zhan Haiyuan Guo y Chengying Chi Xuegang Zhan. "Multiple Scene Sentiment Analysis Based on Chinese Speech and Text". 電腦學刊 33, n.º 1 (febrero de 2022): 165–78. http://dx.doi.org/10.53106/199115992022023301015.

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<p>This paper proposes a multi-scene sentiment analysis model for Chinese speech and text based on CNN-BiGRU-CTC + ERNIE-BiLSTM. The model is applied to the intelligent customer service scenario. While conducting voice interaction, intelligent customer service can obtain the user’s current emotion, to give a more humane answer and improve the user experience. All the training data sets in this paper adopted public data sets such as Aishell-1 and NLPCC 2014, etc.We have been able to achieve a testing accuracy of about 94.5%. The accuracy is improved by 5.24% compared to the latest speech sentiment analysis model that uses audio as a feature. The advantage of this paper is that it adopts the ERNIE language pre-training model to conduct sentiment analysis on speech signals, which still has a good classification accuracy in the case of individual wrong words in speech recognition. </p> <p> </p>

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43

Arigusman, Anggi. "An Analysis of Student’s Narrative Text Writing: An SFL Approach". International Journal of Languages, Literature and Linguistics 4, n.º 2 (junio de 2018): 93–100. http://dx.doi.org/10.18178/ijlll.2018.4.2.156.

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44

Sodhar, Irum Naz, Abdul Hafeez Buller y Suriani Sulaiman. "Morphology-Assisted Sindhi Text Analysis for Natural Language Processing Applications". Indian Journal Of Science And Technology 16, n.º 35 (26 de septiembre de 2023): 2894–901. http://dx.doi.org/10.17485/ijst/v16i35.1719.

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45

Millares Martin, Pablo. "Consultation analysis: use of free text versus coded text". Health and Technology 11, n.º 2 (24 de enero de 2021): 349–57. http://dx.doi.org/10.1007/s12553-020-00517-3.

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46

Emery, Peter G. "Text Classification and Text Analysis in Advances Translation Teaching". Meta 36, n.º 4 (30 de septiembre de 2002): 567–77. http://dx.doi.org/10.7202/002707ar.

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Résumé On étudie d'abord les différentes bases théoriques sur lesquelles se fonde la classification des textes tout en préconisant comme critère prépondérant le domaine ou « contexte social ». On traite des méthodes d'analyse de textes en tenant compte de certaines théories linguistiques. Enfin, on souligne l'importance de l'analyse textuelle dans la pédagogie de la traduction. Les exemples illuslrafifs sont tirés de traductions arabe/anglais.

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47

Migyong Lee. "A Study of Text Difficulty Analysis for Translation Text". Journal of Translation Studies 13, n.º 2 (junio de 2012): 139–64. http://dx.doi.org/10.15749/jts.2012.13.2.006.

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48

Kharinov, Mikhail Vyacheslavovich. "Signal Analysis by Analogy with Text Analysis". SPIIRAS Proceedings, n.º 9 (17 de marzo de 2014): 81. http://dx.doi.org/10.15622/sp.9.3.

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49

Scollon, Ron. "Food and behavior: A Burkean motive analysis of a quasi-medical text". Text & Talk - An Interdisciplinary Journal of Language, Discourse Communication Studies 26, n.º 1 (26 de enero de 2006): 107–25. http://dx.doi.org/10.1515/text.2006.005.

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50

Boromisza-Habashi, David. "Dismantling the antiracist “hate speech” agenda in Hungary: an ethno-rhetorical analysis". Text & Talk - An Interdisciplinary Journal of Language, Discourse & Communication Studies 31, n.º 1 (enero de 2011): 1–19. http://dx.doi.org/10.1515/text.2011.001.

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