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Journal articles on the topic 'Text level'

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1

Kojdan, E., and L. Gogina. "PHONETIC LEVEL OF TEXT STYLISM." National Association of Scientists 3, no. 66 (2021): 33–36. http://dx.doi.org/10.31618/nas.2413-5291.2021.3.66.411.

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The article highlights the importance of the phonetic level of linguistics to identify the stylistic features of the text, both at the level of sound means of artistic expression, and in the phono-pronouncing prosodic manner. The phonetic aspect of the style of discourse serves as a means of social communication, in connection with which it emphasizes the convergence of all language levels in the organization of stylistic coherence of the text, attention is paid to the importance of preserving the author's phonostyle in the practice of translation. The paper reveals the manifestation of phonos
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Watt, J. H. "Level of abstraction structured text." IEEE Transactions on Systems, Man, and Cybernetics 18, no. 4 (1988): 497–505. http://dx.doi.org/10.1109/21.17367.

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Kruh, Louis. "COLLEGE-LEVEL MATH CRYPTO TEXT." Cryptologia 29, no. 3 (2005): 275. http://dx.doi.org/10.1080/01611190508951308.

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Izyumsky, A. B. "Text-book with deepening level." Alma mater. Vestnik Vysshey Shkoly, no. 7 (July 2019): 107–15. http://dx.doi.org/10.20339/am.07-19.107.

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5

Elhoseiny, Mohamed, and Ahmed Elgammal. "Text to multi-level MindMaps." Multimedia Tools and Applications 75, no. 8 (2015): 4217–44. http://dx.doi.org/10.1007/s11042-015-2467-y.

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Allayev, Zafar Mengboyevich. "THE NOTION OF ONEIROSPHERIC TEXT." Multidisciplinary Journal of Science and Technology 4, no. 6 (2024): 688–91. https://doi.org/10.5281/zenodo.12608257.

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This articles discusses and summaries a number of notions related to text linguistics that helps language learners how read texts and analyze them critically, and suggests some useful strategies to improve reading comprehension.
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Fabrizio, Jonathan, Beatriz Marcotegui, and Matthieu Cord. "Text detection in street level images." Pattern Analysis and Applications 16, no. 4 (2013): 519–33. http://dx.doi.org/10.1007/s10044-013-0329-7.

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Murrel, Sharon L., and Thaddeus J. Kowalski. "Monk: A High-Level Text Compiler." AT&T Technical Journal 68, no. 4 (1989): 45–60. http://dx.doi.org/10.1002/j.1538-7305.1989.tb00077.x.

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Nor, Hidayah Zainal Abidin, and Abdul Samat Pathiah. "Requirements identification for distributed agile team communication using high level carotene." Bulletin of Electrical Engineering and Informatics 10, no. 1 (2021): 249–57. https://doi.org/10.11591/eei.v10i1.2031.

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Communication plays an important role to deliver the correct information. However, the communication became challenging especially for agile software teams, which are in geographical distributed. The problem arise when there are exchanging information using unstructured communication platform, misunderstanding on the information communicated and lack of documentation. The aim of this study is to propose a text classification technique for requirements identification in text messages. In this study, we adopted the cascade and cluster classification concept of Carotene that relies on the hash ta
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Ioana, Boghian. "The Metaphor of the 'House as Text' in Victorian Novels." Limbaj si context 2/2015, no. 7 (2017): 66–83. https://doi.org/10.5281/zenodo.495101.

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After the poetic metaphor (of the Aristotelic and classical rhetoric schools), and the linguistic metaphor (belonging to the historical linguistics and the philosophy of language), the metaphor has turned into a central topos in epistemology (M. Black, 1962; P. Ricoeur, 1975; T. van Dijk, 1975; J. Molino, 1979a, 1979b; A. Ortony, 1979 apud Rovenţa-Frumuşani, 2000); the metaphor has ceased, on the one hand, to be a poetic myth, becoming an explanatory principle in science (also in Johansen and Larsen, 2002) and, on the other hand, to restrict itself only to the lexical level, in order to enter
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Yeong, Yin-Lai, Tien-Ping Tan, and Keng Hoon Gan. "A Hybrid of Sentence-Level Approach and Fragment-Level Approach of Parallel Text Extraction from Comparable Text." Procedia Computer Science 161 (2019): 406–14. http://dx.doi.org/10.1016/j.procs.2019.11.139.

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Mason, A. M., and N. C. Snaith. "Symplectic n-level densities with restricted support." Random Matrices: Theory and Applications 05, no. 04 (2016): 1650013. http://dx.doi.org/10.1142/s2010326316500131.

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In this paper, we demonstrate that the alternative form, derived by us in an earlier paper, of the [Formula: see text]-level densities for eigenvalues of matrices from the classical compact group [Formula: see text] is far better suited for comparison with derivations of the [Formula: see text]-level densities of zeros in the family of Dirichlet [Formula: see text]-functions associated with real quadratic characters than the traditional determinantal random matrix formula. Previous authors have found ingenious proofs that the leading order term of the [Formula: see text]-level density of the z
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Ilic Mestric, Ivana, Arvid Kok, Giavid Valiyev, Michael Street, and Peter Lenk. "Aspect Level Sentiment Analysis Methods Applied to Text in Formal Military Reports." Information & Security: An International Journal 46, no. 3 (2020): 227–38. http://dx.doi.org/10.11610/isij.4616.

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Shen, Zhenji, and Zhiyong Xiao. "A Chinese Short Text Similarity Method Integrating Sentence-Level and Phrase-Level Semantics." Electronics 13, no. 24 (2024): 4868. https://doi.org/10.3390/electronics13244868.

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Short text similarity, as a pivotal research domain within Natural Language Processing (NLP), has been extensively utilized in intelligent search, recommendation systems, and question-answering systems. Most existing short-text similarity models focus on aligning the overall semantic content of an entire sentence, often ignoring the semantic associations between individual phrases in the sentence. It is particular in the Chinese context, as synonyms and near-synonyms can cause serious interference in the computation of text similarity. To overcome these limitations, a novel short text similari
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Xu, Shusheng, Xingxing Zhang, Yi Wu, and Furu Wei. "Sequence Level Contrastive Learning for Text Summarization." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 11556–65. http://dx.doi.org/10.1609/aaai.v36i10.21409.

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Contrastive learning models have achieved great success in unsupervised visual representation learning, which maximize the similarities between feature representations of different views of the same image, while minimize the similarities between feature representations of views of different images. In text summarization, the output summary is a shorter form of the input document and they have similar meanings. In this paper, we propose a contrastive learning model for supervised abstractive text summarization, where we view a document, its gold summary and its model generated summaries as diff
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Periyasamy, K. "High-Level Text on Higher Order Logic." IEEE Software 13, no. 1 (1996): 117. http://dx.doi.org/10.1109/ms.1996.476302.

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Katata, Hiroyuki, Tomoko Aono, Yoji Noguchi, Masaki Takakura, and Noritoshi Kakoh. "Reversible coding of multi-level text image." Journal of the Institute of Television Engineers of Japan 44, no. 3 (1990): 338–40. http://dx.doi.org/10.3169/itej1978.44.338.

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18

Barney, George. "Control text is pitched at undergraduate level." Microprocessors and Microsystems 10, no. 4 (1986): 230. http://dx.doi.org/10.1016/0141-9331(86)90158-4.

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19

Nohachevska, Inna, and Liudmyla Komarnitska. "The Typology of Poetic Text Comprehension." Collection of Research Papers "Problems of Modern Psychology" 60 (June 29, 2023): 162–84. http://dx.doi.org/10.32626/2227-6246.2023-60.162-184.

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The purpose of the research is to assess the relevance of the research of psychological features of understanding contemporary poetry by future philolo-gists on the basis of questionnaires; to propose the levels of understanding the text.methods of the research. The following theoretical methods of the re-search were used to solve the tasks formulated in the article: a categorical me­thod, structural and functional methods, the methods of the analysis, systemati-zation, modeling, generalization. The method of organizing empirical research was used as the experimental method.the results of the
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Velleman, Eric, and Thea van der Geest. "Online Test Tool to Determine the CEFR Reading Comprehension Level of Text." Procedia Computer Science 27 (2014): 350–58. http://dx.doi.org/10.1016/j.procs.2014.02.039.

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21

Bikowski, Dawn. "Tapestry Series Level 2." TESOL Journal 10, no. 4 (2001): 35–36. http://dx.doi.org/10.1002/j.1949-3533.2001.tb00052.x.

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Reading 2: Michael Ryall (Student text, Pp. 203) Writing 2: Laurie Blass and Meredith Pike‐Baky: (Student text, Pp. 235)Listening & Speaking 2: Mary McVey Gill and Pamela Hartmann (Student text, Pp. 245)(Also available: Instructor's Manuals and CNN® video clips) Boston, MA: Heinle & Heinle, 2000
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22

Qu, Yadong, Qingfeng Tan, Hongtao Xie, Jianjun Xu, YuXin Wang, and Yongdong Zhang. "Exploring Stroke-Level Modifications for Scene Text Editing." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 2119–27. http://dx.doi.org/10.1609/aaai.v37i2.25305.

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Scene text editing (STE) aims to replace text with the desired one while preserving background and styles of the original text. However, due to the complicated background textures and various text styles, existing methods fall short in generating clear and legible edited text images. In this study, we attribute the poor editing performance to two problems: 1) Implicit decoupling structure. Previous methods of editing the whole image have to learn different translation rules of background and text regions simultaneously. 2) Domain gap. Due to the lack of edited real scene text images, the netwo
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23

Denroche, Charles. "Text metaphtonymy." Metaphor and the Social World 8, no. 1 (2018): 1–24. http://dx.doi.org/10.1075/msw.16011.den.

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Abstract This article starts by looking at the various ways metonymic and metaphoric thinking, as independent phenomena, organize text at discourse level. The literature on metaphor in discourse is classified under three broad categories, ‘metaphor clusters’, ‘metaphor chains’ and ‘extended metaphor’, while the less extensive body of research on metonymy in discourse is analyzed into parallel categories, ‘metonymy clusters’, ‘metonymy chains’ and ‘extended metonymy’. The article goes on to look at the ways in which metonymy-in-discourse and metaphor-in-discourse phenomena combine in making mea
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24

Gopal, Revathi, and Magdelena Long Mering. "Reading With EDSAW Acronym at Tertiary Level." English Education Journal 13, no. 3 (2022): 329–43. http://dx.doi.org/10.24815/eej.v13i3.28351.

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This study aims to explore the use of the EDSAW acronym, a reading strategy in locating contextual clues systematically among tertiary-level students. It takes a quantitative research design to answer the research question, ‘How effective is the use of the EDSAW acronym in comprehending a reading text?’ The sample was selected based on a purposive sampling technique that comprised 10 students from a public university who scored band 4 in the Malaysian University English Test (MUET). The data collection technique involves a pre-test and a post-test. A reading text was given during the pre-test
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25

Lee, Kangwook, Sanggyu Han, and Sung-Hyon Myaeng. "A discourse-aware neural network-based text model for document-level text classification." Journal of Information Science 44, no. 6 (2017): 715–35. http://dx.doi.org/10.1177/0165551517743644.

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Capturing semantics scattered across entire text is one of the important issues for Natural Language Processing (NLP) tasks. It would be particularly critical with long text embodying a flow of themes. This article proposes a new text modelling method that can handle thematic flows of text with Deep Neural Networks (DNNs) in such a way that discourse information and distributed representations of text are incorporate. Unlike previous DNN-based document models, the proposed model enables discourse-aware analysis of text and composition of sentence-level distributed representations guided by the
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26

Dougherty Stahl, Katherine A. "Complex Text or Frustration-Level Text: Using Shared Reading to Bridge the Difference." Reading Teacher 66, no. 1 (2012): 47–51. http://dx.doi.org/10.1002/trtr.01102.

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27

Stafura, Joseph Z., and Charles A. Perfetti. "Word-to-text integration: Message level and lexical level influences in ERPs." Neuropsychologia 64 (November 2014): 41–53. http://dx.doi.org/10.1016/j.neuropsychologia.2014.09.012.

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28

Chen, Jingye, Haiyang Yu, Jianqi Ma, Bin Li, and Xiangyang Xue. "Text Gestalt: Stroke-Aware Scene Text Image Super-resolution." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (2022): 285–93. http://dx.doi.org/10.1609/aaai.v36i1.19904.

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In the last decade, the blossom of deep learning has witnessed the rapid development of scene text recognition. However, the recognition of low-resolution scene text images remains a challenge. Even though some super-resolution methods have been proposed to tackle this problem, they usually treat text images as general images while ignoring the fact that the visual quality of strokes (the atomic unit of text) plays an essential role for text recognition. According to Gestalt Psychology, humans are capable of composing parts of details into the most similar objects guided by prior knowledge. Li
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Liu, Tao, Xiaoyong Du, Yongdong Xu, Minghui Li, and Xiaolong Wang. "Partially Supervised Text Classification with Multi-Level Examples." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 890–95. http://dx.doi.org/10.1609/aaai.v25i1.7969.

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Partially supervised text classification has received great research attention since it only uses positive and unlabeled examples as training data. This problem can be solved by automatically labeling some negative (and more positive) examples from unlabeled examples before training a text classifier. But it is difficult to guarantee both high quality and quantity of the new labeled examples. In this paper, a multi-level example based learning method for partially supervised text classification is proposed, which can make full use of all unlabeled examples. A heuristic method is proposed to as
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Ariyani, Etika. "INDONESIA’S INTERFERENCE IN TRANSLATING TEXT INTO ENGLISH TEXT." Linguistics and ELT Journal 3, no. 1 (2015): 60. http://dx.doi.org/10.31764/leltj.v3i1.818.

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This research is aimed: (1) to reveal that interference of Indonesian language interrupt when the students at the sixth semester of FKIP in Muhammadiyah University of Mataram translate the English narrative past tense text; (2) to know the kinds of factors interrupted the students’ in translating English narrative the text. The population of study were students in FKIP Muhammadiyah University of Mataram. They consist of three classes from class A to C. The number of the population was 64 students so, the writer took only the 17 students as the samples, by using purposive sampling technique. Th
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Bhargav, Rajyagor, and Rakholia Rajnish. "Tri-level handwritten text segmentation techniques for Gujarati language." Indian Journal of Science and Technology 14, no. 7 (2021): 618–27. https://doi.org/10.17485/IJST/v14i7.2146.

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Abstract <strong>Objectives:</strong>&nbsp;To improve the efficiency of tri-level segmentation tasks for handwritten Gujarati text.&nbsp;<strong>Methods:</strong>&nbsp;Using hybrid methods for tri-level segmentation, we have used line, word and character segmentation from the image. This study presents a segmentation paradigm that works with touching characters, slop of the line written on the page, character overlapping, etc. It evaluated on the dataset of 500+ images created by us on different writing sentences by different people. We have used the Horizontal projection technique for line se
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Orlov, Alexei O., Craig S. Lent, Cameron C. Thorpe, Graham P. Boechler, and Gregory L. Snider. "Experimental Test of Landauer's Principle at the Sub-$k_{\text{B}} T$ Level." Japanese Journal of Applied Physics 51 (June 20, 2012): 06FE10. http://dx.doi.org/10.1143/jjap.51.06fe10.

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33

Silvia, Golubițchi. "Methodological Aspects of Multimodal Text Study in Primary School." Journal of Educational Theory and Practice DIDACTICA PRO... 23, no. 2-3 (138-139) (2023): 57–60. https://doi.org/10.5281/zenodo.7974409.

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Multimedia means tend to have a strong impact on students. Multimodal texts are a way of using information technology in primary school, which combine two or more semiotic systems (linguistic, visual, audio, gestural, or spatial) and include several ways of representing reality, which can reach the receiver much easier and more effectively. The multimodal text highlights changes at content, didactic strategy, and goal level. Reading a multimodal text involves understanding, reception, interpretation, reflection, and evaluation, while composing one follows three stages: pre-editing, editing, an
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34

Lee, Minwoo, Yanghoon Kim, and Kyomin Jung. "Text Classification via Sentence-level Graph Convolutional Networks." KIISE Transactions on Computing Practices 25, no. 8 (2019): 397–401. http://dx.doi.org/10.5626/ktcp.2019.25.8.397.

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Ishihara, Manabu, Jun Shirataki, and Shogo Ieiri. "A Text Intelligibility of Three Level Speech Signal." IEEJ Transactions on Electronics, Information and Systems 112, no. 12 (1992): 849–50. http://dx.doi.org/10.1541/ieejeiss1987.112.12_849.

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36

Hadley, Pamela A. "Language Sampling Protocols for Eliciting Text-Level Discourse." Language, Speech, and Hearing Services in Schools 29, no. 3 (1998): 132–47. http://dx.doi.org/10.1044/0161-1461.2903.132.

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The identification of linguistic vulnerability in school-age students is likely to require collecting and analyzing samples of text-level discourse. Text-level discourse produced as part of narrative and expository tasks is more likely to reveal school-age children’s most advanced language abilities and to evoke more communication breakdowns and production errors. This article briefly reviews the research literature establishing the need to sample text-level discourse and identifies several issues for clinicians to consider when constructing their own language sampling protocols. The article c
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J, Saranya, and Arunpriya C. "Survey on Clustering Algorithms for Sentence Level Text." International Journal of Computer Trends and Technology 10, no. 2 (2014): 61–66. http://dx.doi.org/10.14445/22312803/ijctt-v10p111.

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38

Wen, Jiahui, Guangda Zhang, Hongyun Zhang, Wei Yin, and Jingwei Ma. "Speculative text mining for document-level sentiment classification." Neurocomputing 412 (October 2020): 52–62. http://dx.doi.org/10.1016/j.neucom.2020.06.024.

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Li, Wen, Duoqian Miao, and Weili Wang. "Two-level hierarchical combination method for text classification." Expert Systems with Applications 38, no. 3 (2011): 2030–39. http://dx.doi.org/10.1016/j.eswa.2010.07.139.

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Zahedi, Mohammad-hadi, and Mohsen Kahani. "SREC: Discourse-level semantic relation extraction from text." Neural Computing and Applications 23, no. 6 (2012): 1573–82. http://dx.doi.org/10.1007/s00521-012-1109-9.

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41

Li, Wen-Hui, Song Yang, Yan Wang, Dan Song, and Xuan-Ya Li. "Multi-level similarity learning for image-text retrieval." Information Processing & Management 58, no. 1 (2021): 102432. http://dx.doi.org/10.1016/j.ipm.2020.102432.

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42

Sandor, Andras. "Siginification, Meaning, and the Text Level of Language." Indogermanische Forschungen 94, no. 1 (1989): 45–66. http://dx.doi.org/10.1515/if-1989-0106.

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43

Liu, Xinhui, Jifa Chen, and Qiubo Huang. "Instance-Level Weighted Contrast Learning for Text Classification." Applied Sciences 15, no. 8 (2025): 4236. https://doi.org/10.3390/app15084236.

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With the explosion of information, the amount of text data has increased significantly, making text categorization a central area of research in natural language processing (NLP). Traditional machine learning methods are effective, but deep learning models excel in processing semantic information. Models such as CNN, RNN, LSTM, and GRU have emerged as powerful tools for text classification. Pre-trained models such as BERT and GPT have further advanced text categorization techniques. Contrastive learning has become a key research focus aimed at improving classification performance by learning t
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44

Dyachenko, I. N. "Phonetic Level of the Text as the Interpretation Determinant." Philology & Human, no. 1 (July 16, 2021): 7–19. http://dx.doi.org/10.14258/filichel(2021)1-01.

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I.N. Dyachenko. Phonetic Level of the Text as the Interpretation Determinant. The article deals with modern paradigms of the text analysis. The text is considered the result of speech activity. The speech activity is an indispensable part of any individual’s activity. Recently, linguistics emphasizes that language is the way of representation of an individual’s mentality and consciousness. The text understanding is based on the system of concepts. As a result, the perception of the author’s idea is possible. The following approach is topical in the original text analysis. The interpreter is re
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Alkaldi, Wejdan, and Diana Inkpen. "Text Simplification to Specific Readability Levels." Mathematics 11, no. 9 (2023): 2063. http://dx.doi.org/10.3390/math11092063.

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The ability to read a document depends on the reader’s skills and the text’s readability level. In this paper, we propose a system that uses deep learning techniques to simplify texts in order to match a reader’s level. We use a novel approach with a reinforcement learning loop that contains a readability classifier. The classifier’s output is used to decide if more simplification is needed, until the desired readability level is reached. The simplification models are trained on data annotated with readability levels from the Newsela corpus. Our simplification models perform at sentence level,
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Cha, Sunkyo, and Bongkyoo Yoon. "Forecasting the Level and Types of North Korea’s Provocations with Text Mining." Journal of the Korean Institute of Industrial Engineers 49, no. 5 (2023): 441–47. http://dx.doi.org/10.7232/jkiie.2023.49.5.441.

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47

Chan, Ebby Waqqash Mohamad, Mohamad Shariff A. Hamid, Ali Md Nadzalan, and Eliza Hafiz. "Abdominal muscle activation: An EMG study of the Sahrmann five-level core stability test." Hong Kong Physiotherapy Journal 40, no. 02 (2020): 89–97. http://dx.doi.org/10.1142/s1013702520500080.

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Background: Sahrmann five-level core stability test protocol has been used to evaluate the ability of the core muscles to stabilize the spine. However, validation studies on the Sahrmann protocol are limited. Objective: The purpose of this study was to compare the different levels of Sahrmann five-level core stability (levels 1–5) on the muscle activity of rectus abdominis (RA), external oblique (EO), and transverse abdominis/internal oblique (TrA/IO). Methods: Twenty-two asymptomatic male participants aged 21.3[Formula: see text].59 years were recruited. Participants were instructed to perfor
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Widyantoro, Agus, Jamilah Jamilah, and Ari Purnawan. "Text difficulty vs text readability: Students� voices." EduLite: Journal of English Education, Literature and Culture 7, no. 1 (2022): 125. http://dx.doi.org/10.30659/e.7.1.125-136.

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Reading is an essential skill to be mastered, especially by university students and it is the lecturers� responsibility to train their students to develop their reading skill. In order to do so, lecturers will have to develop materials or choose materials from existing books to be used in the teaching learning process. The text difficulty must be appropriate to the students� English proficiency level. This study was aimed at finding the students� perceptions of the difficulty of the texts used in the reading class and the corresponding text readability. This study utilized the survey design an
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Jha, Nitesh Kumar, and Arnab Mitra. "Introducing Word's Importance Level-Based Text Summarization Using Tree Structure." International Journal of Information Retrieval Research 10, no. 1 (2020): 13–33. http://dx.doi.org/10.4018/ijirr.2020010102.

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Text-summarization plays a significant role towards quick knowledge acquisition from any text-based knowledge resource. To enhance the text-summarization process, a new approach towards automatic text-summarization is presented in this article that facilitates level (word importance factor)-based automated text-summarization. An equivalent tree is produced from the directed-graph during the input text processing with WordNet. Detailed investigations further ensure that the execution time for proposed automatic text-summarization, is strictly following a linear relationship with reference to th
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Xiang, Lingyun, Shuanghui Yang, Yuhang Liu, Qian Li, and Chengzhang Zhu. "Novel Linguistic Steganography Based on Character-Level Text Generation." Mathematics 8, no. 9 (2020): 1558. http://dx.doi.org/10.3390/math8091558.

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With the development of natural language processing, linguistic steganography has become a research hotspot in the field of information security. However, most existing linguistic steganographic methods may suffer from the low embedding capacity problem. Therefore, this paper proposes a character-level linguistic steganographic method (CLLS) to embed the secret information into characters instead of words by employing a long short-term memory (LSTM) based language model. First, the proposed method utilizes the LSTM model and large-scale corpus to construct and train a character-level text gene
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