Relevant bibliographies by topics / Understanding of the text

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

Academic literature on the topic 'Understanding of the text'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 June 2025

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Journal articles on the topic "Understanding of the text"

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Rasuljonova, Rasuljonova Saida. "UNDERSTANDING UZBEK TEXTS IN ENGLISH." Journal of Science-Innovative Research in Uzbekistan 2, no. 10 (2024): 318–21. https://doi.org/10.5281/zenodo.13941392.

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In this article includes it should be said that communication between people is carried out through the means of communication texts (and this is recognized by the creators of text linguistics, in general, by the majority of text researchers), limiting the text only to the written form contradicts the existing rules of text theory. After all, it is impossible to imagine that any communication between people takes place only and only in written form.
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Hiebert, Elfrieda H., and P. David Pearson. "Understanding Text Complexity." Elementary School Journal 115, no. 2 (2014): 153–60. http://dx.doi.org/10.1086/678446.

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Goldman, Robert P., and Eugene Charniak. "Probabilistic text understanding." Statistics and Computing 2, no. 2 (1992): 105–14. http://dx.doi.org/10.1007/bf01889589.

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El-Gamal, S. S., and M. M. Esmail. "Understanding clinical narrative text." Medical Informatics 20, no. 2 (1995): 161–73. http://dx.doi.org/10.3109/14639239509025354.

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Gerrig, Richard J. "Models of understanding text." Journal of Pragmatics 30, no. 5 (1998): 637–45. http://dx.doi.org/10.1016/s0378-2166(98)00038-1.

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Banik, Dr Somdev. "Understanding The Fear of the Text in a Post." Indian Journal of Applied Research 3, no. 4 (2011): 225–26. http://dx.doi.org/10.15373/2249555x/apr2013/75.

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Ivgi, Maor, Uri Shaham, and Jonathan Berant. "Efficient Long-Text Understanding with Short-Text Models." Transactions of the Association for Computational Linguistics 11 (2023): 284–99. http://dx.doi.org/10.1162/tacl_a_00547.

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Abstract Transformer-based pretrained language models (LMs) are ubiquitous across natural language understanding, but cannot be applied to long sequences such as stories, scientific articles, and long documents due to their quadratic complexity. While a myriad of efficient transformer variants have been proposed, they are typically based on custom implementations that require expensive pretraining from scratch. In this work, we propose SLED: SLiding-Encoder and Decoder, a simple approach for processing long sequences that re-uses and leverages battle-tested short-text pretrained LMs. Specifically, we partition the input into overlapping chunks, encode each with a short-text LM encoder and use the pretrained decoder to fuse information across chunks (fusion-in-decoder). We illustrate through controlled experiments that SLED offers a viable strategy for long text understanding and evaluate our approach on SCROLLS, a benchmark with seven datasets across a wide range of language understanding tasks. We find that SLED is competitive with specialized models that are up to 50x larger and require a dedicated and expensive pretraining step.
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D.Umanandhini*1, &. S.Manimegalai2. "FUZZY SCORE BASED SHORT TEXT UNDERSTANDING FROM CORPUS DATA USING SEMANTIC DISCOVERY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 12 (2017): 268–73. https://doi.org/10.5281/zenodo.1116682.

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Short text understanding and short text are always more ambiguous. These short texts are produced including Search queries, Tags, Keywords, Conversation or Social posts and containing limited context. Generally short texts do not contain sufficient collection of data to support many state-of-the-art approaches for text mining such as topic modelling. It presents a comprehensive overview of short text understanding. Here we used a novel framework are Text Feature Extraction Algorithm and Fuzzy weighted Vote algorithm First, Text classification based on semantic feature extraction.   Its goal is that use semantic feature extraction to improve the performance of classifier. And second, Fuzzy weighted Vote algorithm is the combination of Fuzzy logic and weighted vote algorithm, which means it generates the fuzzy score and then based on this score the weight is calculated during shortening the text. In experimental results, the novel Feature Extraction and voter has higher safety performance than the previous classification algorithms. This proposed criterion can provide almost accurate safety and also a good range of accessibility. We have proved that in problems where the weighted voting distinguish some alternatives and finds the best alternative. Reduced Computation time comparing to other previous process and schemes.  
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Pulles, Maaike, Jan Berenst, Tom Koole, and Kees de Glopper. "Text formulations as practices of demonstrating understanding in dialogic reading." Text & Talk 41, no. 4 (2021): 515–38. http://dx.doi.org/10.1515/text-2019-0222.

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Abstract This paper examines text formulations in the interaction between peers in primary school during dialogic reading, in inquiry learning settings. In this context pupils collaboratively use information from texts to answer their research questions. The data analyzed include 25 excerpts of pupils demonstrating understanding of text. We used Conversation Analysis to analyze how pupils demonstrate their understanding by the use of text formulations, as a specific type of formulations, and how these formulations function as a bridge between the reading action and the discussion of text content. Parallel to the types of conversational formulations (gist and upshot), we found two practices of demonstrating understanding, namely (1) formulating the gist of relevant text to demonstrate literal understanding, and (2) formulating an upshot to demonstrate how the text contributes to the reading goal. Both types are used to establish shared understanding of text, but focus the discussion as well on what participants find relevant information in the text to further talk about. To reach shared understanding and to use it for next steps, both interactants need to have access to the text in some way. This study contributes to our understanding of how pupils collaboratively use text to build their knowledge.
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Kravchyna, Tetiana. "MODELS OF FOREIGN TEXT UNDERSTANDING." Scientific Notes of Ostroh Academy National University: Psychology Series 1, no. 15 (2022): 14–18. http://dx.doi.org/10.25264/2415-7384-2022-15-14-18.

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The article aims: 1) to analyze the theoretical development of models for understanding a foreign language text from a psychological point of view; 2) to emphasize their contribution to the general theory of understanding; 3) to structure, single out models of comprehension of a foreign language text. It also considers psycholinguistic and cognitive processes that affect the reader’s understanding of a foreign language text, analyzes resonant, constructivist and dynamic approaches to the processes of decoding lexical and grammatical information and inference of the meaning of the text. The main idea of the resonant approach is that in the process of reading, the reader automatically activates and removes from long-term memory all the information that corresponds to the semantic and phonological material of the text. The constructivist approach assumes that text comprehension is based on cognitively processed meanings of lexical units, which are always constructed in working memory depending on the content of the context and verbal/nonverbal experience of the reader. Currently, a dynamic approach to modelling the foreign text comprehension dominates, in which the resonant process and construction are considered to interact on a stage basis: the initial broad activation of lexical and grammatical concepts is accompanied by cognitive processing and extraction of relevant meanings from different knowledge structures in the long run. Thus, all these processes actively interact with the individual cognitive base of the reader, which does not lead to the creation of a literal, but to a more complex mental representation of a foreign text. The created representation depends not only on the previous knowledge of the reader, but also enriches his frames, which contain information about the typical and the possible in relevant situations.
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Dissertations / Theses on the topic "Understanding of the text"

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Al-Khonaizi, Mohammed Taqi. "Natural Arabic language text understanding." Thesis, University of Greenwich, 1999. http://gala.gre.ac.uk/6096/.

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The most challenging part of natural language understanding is the representation of meaning. The current representation techniques are not sufficient to resolve the ambiguities, especially when the meaning is to be used for interrogation at a later stage. Arabic language represents a challenging field for Natural Language Processing (NLP) because of its rich eloquence and free word order, but at the same time it is a good platform to capture understanding because of its rich computational, morphological and grammar rules. Among different representation techniques, Lexical Functional Grammar (LFG) theory is found to be best suited for this task because of its structural approach. LFG lays down a computational approach towards NLP, especially the constituent and the functional structures, and models the completeness of relationships among the contents of each structure internally, as well as among the structures externally. The introduction of Artificial Intelligence (AI) techniques, such as knowledge representation and inferencing, enhances the capture of meaning by utilising domain specific common sense knowledge embedded in the model of domain of discourse and the linguistic rules that have been captured from the Arabic language grammar. This work has achieved the following results: (i) It is the first attempt to apply the LFG formalism on a full Arabic declarative text that consists of more than one paragraph. (ii) It extends the semantic structure of the LFG theory by incorporating a representation based on the thematic-role frames theory. (iii) It extends to the LFG theory to represent domain specific common sense knowledge. (iv) It automates the production process of the functional and semantic structures. (v) It automates the production process of domain specific common sense knowledge structure, which enhances the understanding ability of the system and resolves most ambiguities in subsequent question-answer sessions.
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Sabsono, Fatimah Ilona Asa. "Understanding Customer Problems through Text Categorisation." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-372180.

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Customer problem is a common problem that needs to be handled in the company that provides support to their customer. Abundant data that it produced makes it inefficient to do it manually, which makes machine learning as an approach that could help to solve it. This project achieved a suitable approach of classifying a customer problem using text categorisation. This particular dataset is solvable when using Term Frequency-Inverse Document Frequency and one-hot encoding to generate the feature and use Logistic Regression as the classifier. Three measurement metrics, named F1 weighted score, Geometric Mean, and Indexed Balance Accuracy, was used to measure this imbalanced dataset.
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Sætre, Rune. "GeneTUC: Natural Language Understanding in Medical Text." Doctoral thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2006. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-545.

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<p>Natural Language Understanding (NLU) is a 50 years old research field, but its application to molecular biology literature (BioNLU) is a less than 10 years old field. After the complete human genome sequence was published by Human Genome Project and Celera in 2001, there has been an explosion of research, shifting the NLU focus from domains like news articles to the domain of molecular biology and medical literature. BioNLU is needed, since there are almost 2000 new articles published and indexed every day, and the biologists need to know about existing knowledge regarding their own research. So far, BioNLU results are not as good as in other NLU domains, so more research is needed to solve the challenges of creating useful NLU applications for the biologists.</p><p>The work in this PhD thesis is a “proof of concept”. It is the first to show that an existing Question Answering (QA) system can be successfully applied in the hard BioNLU domain, after the essential challenge of unknown entities is solved. The core contribution is a system that discovers and classifies unknown entities and relations between them automatically. The World Wide Web (through Google) is used as the main resource, and the performance is almost as good as other named entity extraction systems, but the advantage of this approach is that it is much simpler and requires less manual labor than any of the other comparable systems.</p><p>The first paper in this collection gives an overview of the field of NLU and shows how the Information Extraction (IE) problem can be formulated with Local Grammars. The second paper uses Machine Learning to automatically recognize protein name based on features from the GSearch Engine. In the third paper, GSearch is substituted with Google, and the task in this paper is to extract all unknown names belonging to one of 273 biomedical entity classes, like genes, proteins, processes etc. After getting promising results with Google, the fourth paper shows that this approach can also be used to retrieve interactions or relationships between the named entities. The fifth paper describes an online implementation of the system, and shows that the method scales well to a larger set of entities.</p><p>The final paper concludes the “proof of concept” research, and shows that the performance of the original GeneTUC NLU system has increased from handling 10% of the sentences in a large collection of abstracts in 2001, to 50% in 2006. This is still not good enough to create a commercial system, but it is believed that another 40% performance gain can be achieved by importing more verb templates into GeneTUC, just like nouns were imported during this work. Work has already begun on this, in the form of a local Masters Thesis.</p>
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Guo, Runli. "Proper name knowledge acquisition for text understanding." Thesis, University of Surrey, 2002. http://epubs.surrey.ac.uk/800039/.

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Lindén, Johannes. "Extracting Text into Meta-Data : Improving machine text-understanding of news-media articles." Licentiate thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-41775.

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Society is constantly in need of information. It is important to consume event-based information of what is happening around us as well as facts and knowledge. As society grows, the amount of information to consume grows with it. This thesis demonstrates one way to extract and represent knowledge from text in a machine-readable way for news media articles. Three objectives are considered when developing a machine learning system to retrieve categories, entities, relations and other meta-data from text paragraphs. The first is to sort the terminology by topic; this makes it easier for machine learning algorithms to understand the text and the unique words used. The second objective is to construct a service for use in production, where scalability and performance are evaluated. Features are implemented to iteratively improve the model predictions, and several versions are run at the same time to, for example, compare them in an A/B test. The third objective is to further extract the gist of what is expressed in the text. The gist is extracted in the form of triples by connecting two related entities using a combination of natural language processing algorithms.  The research presents a comparison between five different auto categorization algorithms, and an evaluation of their hyperparameters and how they would perform under the pressure of thousands of big, concurrent predictions. The aim is to build an auto-categorization system that can be used in the news media industry to help writers and journalists focus more on the story rather than filling in meta-data for each article. The best-performing algorithm is a Bidirectional Long-Short-Term-Memory neural network. Three different information extraction algorithms for extracting the gist of paragraphs are also compared. The proposed information extraction algorithm supports extracting information from texts in multiple languages with competitive accuracy compared with the state-of-the-art OpenIE and MinIE algorithms that can extract information in a single language. The use of the multi-linguistic models helps local-news media to write articles in different languages as a help to integrate immigrants  into the society.<br><p>Vid tidpunkten för presentationen var följande delarbeten opublicerade: delarbete 4 inskickat.</p><p>At the time of the public defence the following papers were unpublished: paper 4 submitted.</p>
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Hashimoto, Chikara. "Knowledge Acquisition from the Web for Text Understanding." 京都大学 (Kyoto University), 2011. http://hdl.handle.net/2433/151931.

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Cohen, F. "TASS - Text Analysis System for Understanding News Stories." Thesis, University of Reading, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383567.

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Ferreira, Sidnéa Nunes. "Understanding text-image relationships in Newsweek cover stories." Florianópolis, SC, 2003. http://repositorio.ufsc.br/xmlui/handle/123456789/85174.

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Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro de Comunicação e Expressão. Programa de Pós-Graduação em Letras/Inglês e Literatura Correspondente.<br>Made available in DSpace on 2012-10-20T16:03:13Z (GMT). No. of bitstreams: 1 210520.pdf: 6154986 bytes, checksum: 19acef4a425b924af72b8f876e19cbd6 (MD5)<br>Estudo das relações texto-imagem em artigos de capa da revista Newsweek, visando contribuir para o entendimento de como significados multimodais são construídos. A partir da macro-análise de 24 artigos de capa, identificam-se os principais componentes verbais e visuais da estrutura deste gênero multimodal. Enquanto que a partir da micro-análise de dois artigos de capa, investiga-se como os modos verbal e visual constroem significados funcionais e como estes significados modulam, daí construindo o significado central dos artigos de capa. Baseado nos resultados da investigação proposta, o estudo aponta para três urgentes necessidades pedagógicas.
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Barth, Elaine Maria Luz. "The effects of text structure instruction on efl reader's understanding of expository texts." reponame:Repositório Institucional da UFSC, 1990. https://repositorio.ufsc.br/xmlui/handle/123456789/157653.

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Dissertação (mestrado) - Universidade Federal de Santa Catarina. Centro de Comunicação e Expressão<br>Made available in DSpace on 2016-01-08T16:51:35Z (GMT). No. of bitstreams: 1 80067.pdf: 4788847 bytes, checksum: e2967ec153e31fb0d4401ad3f98eadc2 (MD5) Previous issue date: 1990
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Nikonova, E., А. Pronina, and J. Muzzarelli. "The problem of understanding written texts (EFL engineering students)." Thesis, Sumy State University, 2015. http://essuir.sumdu.edu.ua/handle/123456789/39134.

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Books on the topic "Understanding of the text"

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Herzog, Otthein, and Claus-Rainer Rollinger, eds. Text Understanding in LILOG. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-54594-8.

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Sabourin, Conrad. Computational text understanding: Bibliography. Infolingua, 1994.

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K, Britton Bruce, and Graesser Arthur C, eds. Models of understanding text. Lawrence Erlbaum Associates, 1996.

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MIND, ed. Understanding mental illness [Bengali text]. MIND, 1987.

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Kim, H. G. Text analysis: As a means of understanding translated texts. UMIST, 1996.

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Behan, Kate. Understanding information technology: Text, readings, andcases. 2nd ed. Prentice Hall, 1990.

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Seyler, Dorothy U. Understanding argument: A text with readings. McGraw-Hill, 1994.

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Behan, Kate. Understanding information technology: Text, readings, and cases. 2nd ed. Prentice Hall, 1990.

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William, Pinar, and Reynolds William M. 1953-, eds. Understanding curriculum as phenomenological and deconstructed text. Teachers College Press, 1992.

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M, Reynolds William, and William F. Pinar. Understanding curriculum as phenomenological and deconstructed text. Educators International Press, 2016.

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Book chapters on the topic "Understanding of the text"

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Les, Zbigniew, and Magdalena Les. "Understanding Text." In Studies in Computational Intelligence. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14197-8_10.

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Sharan, Kishori. "Understanding Text Nodes." In Learn JavaFX 8. Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-1142-7_18.

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Goldman, R. P., and E. Charniak. "Probabilistic text understanding." In Artificial Intelligence Frontiers in Statistics. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4537-2_22.

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Sharan, Kishori, and Peter Späth. "Understanding Text Nodes." In Learn JavaFX 17. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7848-2_15.

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Mauldin, Michael L. "Conceptual Understanding of Text." In Conceptual Information Retrieval. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4004-5_3.

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Sarkar, Dipanjan. "Processing and Understanding Text." In Text Analytics with Python. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2388-8_3.

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Sarkar, Dipanjan. "Processing and Understanding Text." In Text Analytics with Python. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4354-1_3.

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Al-Khonaizi, M., M. Al-A'ali, and A. Al-Zobaidie. "Understanding natural Arabic text." In Human Computer Interaction. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-57312-7_87.

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Bókay, Antal. "Understanding, text, and coherence." In Text and Discourse Connectedness. John Benjamins Publishing Company, 1989. http://dx.doi.org/10.1075/slcs.16.30bok.

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Klyatskin, Valery I. "Statistical Characteristics of a Random Velocity Field $$\text {u}(\text {r}, t)$$." In Understanding Complex Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56922-2_4.

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Conference papers on the topic "Understanding of the text"

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Liang, Min, Jia-Wei Ma, Xiaobin Zhu, Jingyan Qin, and Xu-Cheng Yin. "LayoutFormer: Hierarchical Text Detection Towards Scene Text Understanding." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01483.

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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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Wen, Weiwei, and Lingzhi Liao. "Video understanding with image, audio, and text." In Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), edited by Qinghua Lu and Weishan Zhang. SPIE, 2024. http://dx.doi.org/10.1117/12.3049519.

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Bharadwaj, Tushar, Swarit Ajay, and Sartaj Ahmad. "Harnessing Text Analysis for Automated Document Understanding." In 2024 Second International Conference on Advances in Information Technology (ICAIT). IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690743.

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Jeyaraj, Manuela, and Sarah Delany. "An Explainable Approach to Understanding Gender Stereotype Text." In Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.gebnlp-1.4.

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Nguyen-Truong, Hai, E.-Ro Nguyen, Tuan-Anh Vu, Minh-Triet Tran, Binh-Son Hua, and Sai-Kit Yeung. "Vision-Aware Text Features in Referring Image Segmentation: From Object Understanding to Context Understanding." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00488.

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Vertanen, Keith, and Per Ola Kristensson. "Automatic selection of recognition errors by respeaking the intended text." In Understanding (ASRU). IEEE, 2009. http://dx.doi.org/10.1109/asru.2009.5373347.

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Murakami, Hiroko, Koichi Shinoda, and Sadaoki Furui. "Designing text corpus using phone-error distribution for acoustic modeling." In Understanding (ASRU). IEEE, 2011. http://dx.doi.org/10.1109/asru.2011.6163929.

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Wutiwiwatchai, Chai, Ausdang Thangthai, Ananlada Chotimongkol, Chatchawarn Hansakunbuntheung, and Nattanun Thatphithakkul. "Accent level adjustment in bilingual Thai-English text-to-speech synthesis." In Understanding (ASRU). IEEE, 2011. http://dx.doi.org/10.1109/asru.2011.6163947.

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Bellegarda, Jerome R. "Sentiment analysis of text-to-speech input using latent affective mapping." In Understanding (ASRU). IEEE, 2011. http://dx.doi.org/10.1109/asru.2011.6163948.

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Reports on the topic "Understanding of the text"

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Weischedel, Ralph, Damaris Ayuso, Sean Boisen, Heidi Fox, and Robert Ingria. A New Approach to Text Understanding. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada458079.

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Hobbs, Jerry R. TACITUS: Text Understanding for Strategic Computing. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada230607.

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Randazzese, Lucien. Helios: Understanding Solar Evolution Through Text Analytics. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1336902.

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Meyers, A., B. M. Sundheim, and T. W. Wadsworth. VOX (Vocabulary Extension) Naval Text Understanding System. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada166697.

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Voss, L. L., David E. Wilkins, David Israel, et al. Faust: Flexible Acquistion and Understanding System for Text. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada588330.

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Butler, Frances A., Eva L. Baker, Tine Falk, Howard Herl, Younghee Jang, and Patricia Mutch. Benchmarking Text Understanding Systems to Human Performance: An Exploration. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada233306.

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Holdsworth, Clark. Understanding text recycling and how to avoid self-plagiarism. Peeref, 2023. http://dx.doi.org/10.54985/peeref.2304w2441434.

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Kakulla, Brittne. Technology and the 50-Plus: Understanding Text Trends Among Racially and Ethnically Diverse Adults. AARP Research, 2024. http://dx.doi.org/10.26419/res.00772.004.

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Knapp, S. P., J. E. Athey, and A. L. Nash. Understanding your radon test results. Alaska Division of Geological & Geophysical Surveys, 2020. http://dx.doi.org/10.14509/30467.

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Yatsymirska, Mariya. MODERN MEDIA TEXT: POLITICAL NARRATIVES, MEANINGS AND SENSES, EMOTIONAL MARKERS. Ivan Franko National University of Lviv, 2022. http://dx.doi.org/10.30970/vjo.2022.51.11411.

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Abstract:
The article examines modern media texts in the field of political journalism; the role of information narratives and emotional markers in media doctrine is clarified; verbal expression of rational meanings in the articles of famous Ukrainian analysts is shown. Popular theories of emotions in the process of cognition are considered, their relationship with the author’s personality, reader psychology and gonzo journalism is shown. Since the media text, in contrast to the text, is a product of social communication, the main narrative is information with the intention of influencing public opinion. Media text implies the presence of the author as a creator of meanings. In addition, media texts have universal features: word, sound, visuality (stills, photos, videos). They are traditionally divided into radio, TV, newspaper and Internet texts. The concepts of multimedia and hypertext are related to online texts. Web combinations, especially in political journalism, have intensified the interactive branching of nonlinear texts that cannot be published in traditional media. The Internet as a medium has created the conditions for the exchange of ideas in the most emotional way. Hence Gonzo’s interest in journalism, which expresses impressions of certain events in words and epithets, regardless of their stylistic affiliation. There are many such examples on social media in connection with the events surrounding the Wagnerians, the Poroshenko case, Russia’s new aggression against Ukraine, and others. Thus, the study of new features of media text in the context of modern political narratives and emotional markers is important in media research. The article focuses review of etymology, origin and features of using lexemes “cмисл (meaning)” and “сенс (sense)” in linguistic practice of Ukrainians results in the development of meanings and functional stylistic coloring in the usage of these units. Lexemes “cмисл (meaning)” and “сенс (sense)” are used as synonyms, but there are specific fields of meanings where they cannot be interchanged: lexeme “сенс (sense)” should be used when it comes to reasonable grounds for something, lexeme “cмисл (meaning)” should be used when it comes to notion, concept, understanding. Modern political texts are most prominent in genres such as interviews with politicians, political commentaries, analytical articles by media experts and journalists, political reviews, political portraits, political talk shows, and conversations about recent events, accompanied by effective emotional narratives. Etymologically, the concept of “narrative” is associated with the Latin adjective “gnarus” – expert. Speakers, philosophers, and literary critics considered narrative an “example of the human mind.” In modern media texts it is not only “story”, “explanation”, “message techniques”, “chronological reproduction of events”, but first of all the semantic load and what subjective meanings the author voices; it is a process of logical presentation of arguments (narration). The highly professional narrator uses narration as a “method of organizing discourse” around facts and impressions, impresses with his political erudition, extraordinary intelligence and creativity. Some of the above theses are reflected in the following illustrations from the Ukrainian media: “Culture outside politics” – a pro-Russian narrative…” (MP Gabibullayeva); “The next will be Russia – in the post-Soviet space is the Arab Spring…” (journalist Vitaly Portnikov); “In Russia, only the collapse of Ukraine will be perceived as success” (Pavel Klimkin); “Our army is fighting, hiding from the leadership” (Yuri Butusov).
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