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Relevant bibliographies by topics / Automatic analysis of texts

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Academic literature on the topic 'Automatic analysis of texts'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Automatic analysis of texts"

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DIERKS, K. "Automatic Stylistic Analysis of Lyrical Texts." Literary and Linguistic Computing 1, no. 3 (1986): 129–35. http://dx.doi.org/10.1093/llc/1.3.129.

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Archer, Wendy, Stefano Consiglio, Paolo Ferri, Luca Pareschi, and Silvio Peroni. "Call for papers: Automatic understanding of texts in social and computer sciences." puntOorg International Journal 1, no. 1 (2019): 1. http://dx.doi.org/10.19245/25.05.cfp.05.

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Over the last 20 years, the use of automated and semi-automated techniques for extracting meanings from text have been widely debated in the social sciences. Automated and semi-automated techniques can be employed in all research phases: data collection (e.g. scraping), data cleaning (e.g. lemmatization of words), analysis (e.g. Named Entity Recognition, Part-of-speech Tagging, Topic Modeling, Keyword Analysis, Semantic Network Analysis, Sentiment Analysis), and visualization. Far from forcing epistemological choices, these techniques can be inductively used to deal with big corpora of data, i

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Mandravickaitė, Justina, Eglė Rimkienė, Danguolė Kotryna Kapkan, Danguolė Kalinauskaitė, and Tomas Krilavičius. "Automatic Simplification of Lithuanian Administrative Texts." Algorithms 17, no. 11 (2024): 533. http://dx.doi.org/10.3390/a17110533.

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Text simplification reduces the complexity of text while preserving essential information, thus making it more accessible to a broad range of readers, including individuals with cognitive disorders, non-native speakers, children, and the general public. In this paper, we present experiments on text simplification for the Lithuanian language, aiming to simplify administrative texts to a Plain Language level. We fine-tuned mT5 and mBART models for this task and evaluated the effectiveness of ChatGPT as well. We assessed simplification results via both quantitative metrics and qualitative evaluat

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Zuban, Oksana. "Automatic Morphemic Analysis in the Corpus of the Ukrainian Language: Results and Prospects." Journal of Linguistics/Jazykovedný casopis 68, no. 2 (2017): 415–25. http://dx.doi.org/10.1515/jazcas-2017-0051.

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Abstract The article describes theoretical issues, principles of constructing and functioning of the Automated System of Morphemic and Derivational Analysis (ASMDA). The ASMDA system performs the following functions: 1) information system; 2) automatic morphemic annotation of text; 3) automatic linguistic constructor for frequency dictionaries. Description of the use of ASMDA as an automatic morphemic analyser of Ukrainian texts’ lexicon is in the centre of attention; this article also describes structure as well as search and classification options of electronic morphemic dictionaries present

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Dmitrishin, A. N., V. D. Revina, V. I. Rusnak, Al-dr A. Khoroshilov, and Al-ey A. Khoroshilov. "METHODS OF AUTOMATED CREATION OF THEMATIC ONTOLOGY." Informatization and communication, no. 1 (March 20, 2019): 28–35. http://dx.doi.org/10.34219/2078-8320-2019-10-1-28-35.

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The article describes the methods, the procedures and technology of automated creation of thematic ontology based on automatic processing and semantic analysis of unstructured normative-technical texts and texts of draft documents and determining the semantic relation between detected by concept names. For an application this method necessary to possess procedures complex of automatic semantic the text processing. The article describes the hybrid methods of automated creation of technologies, based for the use of program-linguistic platform of MetaFraz. The research was supported by the Russia

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Myznikov, P. V. "Case-Based Reasoning in Automatic Analysis of News Texts." Vestnik NSU. Series: Information Technologies 15, no. 2 (2017): 59–65. http://dx.doi.org/10.25205/1818-7900-2017-15-2-59-65.

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Balabanova, Elisaveta. "Tagging, Morphological Dictionary in Electronic Form, Lemmatization and Normalization of Texts." Izdatel XXII, no. 2 (2020): 48–55. https://doi.org/10.70300/cyl5dcubw2zzthrd8.

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The paper deals with the process of automatic morphological analysis. The issues of morphology and the operations in it are briefly discussed. The morphological dictionary in electronic form is discussed and also the process of automatic morphological analysis in NLP. The problems, encountered when doing automatic morphological analysis, are reviewed. The processes of automatic normalization and lemmatization are also discussed briefly.

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Ugryn, Tetiana. "AUTOMATIC TEXT SUMMARIZATION: PROBLEMS AND PERSPECTIVES." Naukovì zapiski Nacìonalʹnogo unìversitetu «Ostrozʹka akademìâ». Serìâ «Fìlologìâ» 1, no. 17(85) (2023): 96–101. http://dx.doi.org/10.25264/2519-2558-2023-17(85)-96-101.

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The present paper focusses on the automatic text summarization (AS), the analysis of linguistic problems related to it and the ways to overcome them, as well as on the perspectives of using some natural language processing computer programs. The author carries out a comparative analysis of two AS programs, MSWord2003 and Pertinence Summarizer, for literary, journalistic and scientific texts. The chosen methodology of comparative analysis allows not only to single out the peculiarities and limitations of each program, but also to make some general conclusions about the problems existing in the

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Misini, Arta, Ercan Canhasi, Arbana Kadriu, and Endrit Fetahi. "Automatic authorship attribution in Albanian texts." PLOS ONE 19, no. 10 (2024): e0310057. http://dx.doi.org/10.1371/journal.pone.0310057.

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Automatic authorship identification is a challenging task that has been the focus of extensive research in natural language processing. Regardless of the progress made in attributing authorship, the need for corpora in under-resourced languages impedes advancing and examining present methods. To address this gap, we investigate the problem of authorship attribution in Albanian. We introduce a newly compiled corpus of Albanian newsroom columns and literary works and analyze machine-learning methods for detecting authorship. We create a set of hand-crafted features targeting various categories (

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Salton, G., J. Allan, C. Buckley, and A. Singhal. "Automatic Analysis, Theme Generation, and Summarization of Machine-Readable Texts." Science 264, no. 5164 (1994): 1421–26. http://dx.doi.org/10.1126/science.264.5164.1421.

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Dissertations / Theses on the topic "Automatic analysis of texts"

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Cowie, James Reid. "Automatic analysis of descriptive texts." Thesis, University of Strathclyde, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387066.

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Sardinha, Antonio Paulo Berber. "Automatic identification of segments in written texts." Thesis, University of Liverpool, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364227.

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Törnfeldt, Tobias. "Graph Similarity, Parallel Texts, and Automatic Bilingual Lexicon Acquisition." Thesis, Linköping University, Department of Mathematics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11550.

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<p>In this masters’ thesis report we present a graph theoretical method used for automatic bilingual lexicon acquisition with parallel texts. We analyze the concept of graph similarity and give an interpretation, of the parallel texts, connected to the vector space model. We represent the parallel texts by a directed, tripartite graph and from here use the corresponding adjacency matrix, A, to compute the similarity of the graph. By solving the eigenvalue problem ρS = ASAT + ATSA we obtain the self-similarity matrix S and the Perron root ρ. A rank k approximation of the self-similarity matrix

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AMENDUNI, FRANCESCA. "Defining and Assessing Critical Thinking: toward an automatic analysis of HiEd students’ written texts." Doctoral thesis, Università di Foggia, 2021. https://hdl.handle.net/11369/425168.

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L'obiettivo principale di questa tesi di dottorato è testare, attraverso due studi empirici, l'affidabilità di un metodo volto a valutare automaticamente le manifestazioni del Pensiero Critico (CT) nei testi scritti da studenti universitari. Gli studi empirici si sono basati su una review critica della letteratura volta a proporre una nuova classificazione per sistematizzare le diverse definizioni di CT e i relativi approcci teorici. La review esamina anche la relazione tra le diverse definizioni di CT e i relativi metodi di valutazione. Dai risultati emerge la necessità di concentrarsi s

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Wu, Zimin. "A partial syntactic analysis-based pre-processor for automatic indexing and retrieval of Chinese texts." Thesis, Loughborough University, 1992. https://dspace.lboro.ac.uk/2134/13685.

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Automatic indexing is the automatic creation of a text surrogate, normally keywords or phrases, to represent the original text. In the current English text retrieval systems, this process of content representation is accomplished by extracting words using spaces and punctuations as word delimiters. The same technique cannot easily be applied to Chinese texts which contain no obvious word boundaries; they appear to be a linear sequence of non-spaced or equally spaced ideographic characters and thenumber of characters in words varies. The solution to the problem lies in morphological and syntact

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Warintarawej, Pattaraporn. "Automatic Analysis of Blend Words." Thesis, Montpellier 2, 2013. http://www.theses.fr/2013MON20020.

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Mélanger des parties de mots est une façon qui peut sembler étonnante pour produire de nouvelles formes linguistiques. Cela est devenu une manière très utilisée pour inventer des noms pour le quotidien, les noms de marque, les noms utilisés dans les codes informatiques des logiciels, par exemple avec alicament (aliment and médicament), aspivenin (aspirer and venin). Il existe plusieurs façon de mélanger des mots pour en former d'autres, ce qui rend difficile l'analyse des mots produits. Dans cette thèse, nous nous proposons une approche d'analyse automatique des évocations de mots produits à l

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Benbrahim, Mohamed. "Automatic text summarisation through lexical cohesion analysis." Thesis, University of Surrey, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.309200.

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Wang, Wenbo. "Automatic Emotion Identification from Text." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1440974400.

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Kay, Roderick Neil. "Text analysis, summarising and retrieval." Thesis, University of Salford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360435.

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Mlynarski, Angela, and University of Lethbridge Faculty of Arts and Science. "Automatic text summarization in digital libraries." Thesis, Lethbridge, Alta. : University of Lethbridge, Faculty of Arts and Science, 2006, 2006. http://hdl.handle.net/10133/270.

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A digital library is a collection of services and information objects for storing, accessing, and retrieving digital objects. Automatic text summarization presents salient information in a condensed form suitable for user needs. This thesis amalgamates digital libraries and automatic text summarization by extending the Greenstone Digital Library software suite to include the University of Lethbridge Summarizer. The tool generates summaries, nouns, and non phrases for use as metadata for searching and browsing digital collections. Digital collections of newspapers, PDFs, and eBooks were created

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Books on the topic "Automatic analysis of texts"

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Salton, Gerard. Automatic text processing: The transformation, analysis, and retrieval of information by computer. Addison-Wesley, 1989.

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Salton, Gerard. Automatic text processing: The transformation, analysis, and retrieval of information by computer. Addison-Wesley, 1988.

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Schweighofer, Erich. Legal knowledge representation: Automatic text analysis in public international and European law. Kluwer Law International, 1999.

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Wilson, Andrew. Towards an integration of content analysis and discourse analysis: The automatic linkage of key relations in text. Unit for Computer Research on the English Language, 1993.

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Sabourin, Conrad. Computational linguistics in information science: Information retrieval (full-text or conceptual), automatic indexing, text abstraction, content analysis, information extraction, query languages : bibliography. Infolingua, 1994.

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Cunningham, C. C. Performance optimization of a rotor alone Nacelle for acoustic fan testing. National Aeronautics and Space Administration, Glenn Research Center, 2000.

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Yin, Heng, and Dawn Song. Automatic Malware Analysis. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-5523-3.

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Pêcheux, Michel. Automatic discourse analysis. Rodopi, 1995.

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T, Corns Warren, and Foreman James K, eds. Automatic chemical analysis. 2nd ed. Taylor & Francis, 1996.

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Cases, Miguel Valcárcel. Automatic methods of analysis. Elsevier, 1988.

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Book chapters on the topic "Automatic analysis of texts"

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Mehl, Matthias R., and Alastair J. Gill. "Automatic text analysis." In Advanced methods for conducting online behavioral research. American Psychological Association, 2010. http://dx.doi.org/10.1037/12076-008.

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D’Silva, Jovi, Uzzal Sharma, and Chaitali More. "Automatic Text Summarization of Konkani Texts Using Latent Semantic Analysis." In International Conference on Innovative Computing and Communications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2821-5_37.

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Fishel, Mark, Ondřej Bojar, Daniel Zeman, and Jan Berka. "Automatic Translation Error Analysis." In Text, Speech and Dialogue. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23538-2_10.

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Loukachevitch, Natalia. "Automatic Sentiment Analysis of Texts: The Case of Russian." In The Palgrave Handbook of Digital Russia Studies. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42855-6_28.

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AbstractThe chapter considers the problems of automatic sentiment analysis of texts including processing multiple opinions, implicit and explicit sentiment, ambiguity of sentiment words, sentiment modifiers, irreal context, comparisons, et cetera. Main approaches to sentiment analysis are briefly presented, including the types of sentiment vocabularies. Most attention is given to Russian-specific components of automatic sentiment analysis: publicly available vocabularies and sentiment-related shared tasks.

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Kubát, Miroslav, Ján Mačutek, Radek Čech, and Michaela Nogolová. "Automatic Genre Classification of Czech Texts Based on Syntactic Functions." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55917-4_13.

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Unold, Olgierd. "Automatic Analysis of Natural Language Texts in Man-Machine Communication." In Systems Development Methods for the Next Century. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-5915-3_16.

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Salton, Gerard, James Allan, Chris Buckley, and Amit Singhal. "Automatic Analysis, Theme Generation, and Summarization of Machine-Readable Texts." In Information Retrieval and Hypertext. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1373-1_3.

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Silberztein, Max. "Joe Loves Lea: Transformational Analysis of Direct Transitive Sentences." In Automatic Processing of Natural-Language Electronic Texts with NooJ. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42471-2_5.

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Asthana, Ayushi, and Prajakta Soman. "Automatic Distress Analysis Using Text Mining." In Proceedings of International Conference on Data Science and Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5120-5_12.

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Vinosh Babu, J., and S. Baskaran. "Automatic Language Identification Using Multivariate Analysis." In Computational Linguistics and Intelligent Text Processing. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30586-6_89.

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Conference papers on the topic "Automatic analysis of texts"

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Abjalova, Manzura, Eşref Adalı, and Munojot Adilova. "The Process of Lemmatization and Stemming in the Automatic Morphological Analysis of Uzbek Texts." In 2024 9th International Conference on Computer Science and Engineering (UBMK). IEEE, 2024. https://doi.org/10.1109/ubmk63289.2024.10773506.

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Xie, Bei, and Mingzhu Luo. "Analysis and Research on Automatic Text Classification Algorithm Based on Machine Learning." In 2024 6th International Conference on Communications, Information System and Computer Engineering (CISCE). IEEE, 2024. http://dx.doi.org/10.1109/cisce62493.2024.10652605.

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Hazem, Amir, Béatrice Daille, Dominique Stutzmann, Jacob Currie, and Christine Jacquin. "Towards Automatic Variant Analysis of Ancient Devotional Texts." In Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-4730.

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Chammas, Edgard, Laurence Likforman-Sulem, and Chafic Mokbel. "Stroke width exploitation to improve automatic recognition of Arabic handwritten texts." In 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR). IEEE, 2017. http://dx.doi.org/10.1109/asar.2017.8067763.

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Kotiurova, Irina, and Polina Trenina. "Comparative Analysis of Automatic POS Taggers Applied to German Learner Texts." In 2022 31st Conference of Open Innovations Association (FRUCT). IEEE, 2022. http://dx.doi.org/10.23919/fruct54823.2022.9770886.

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Budhi, Gregorius Satia, Raymond Chiong, Ilung Pranata, and Zhongyi Hu. "Predicting rating polarity through automatic classification of review texts." In 2017 IEEE Conference on Big Data and Analytics (ICBDA). IEEE, 2017. http://dx.doi.org/10.1109/icbdaa.2017.8284101.

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Kim, Sanghee, Rob H. Bracewell, and Ken M. Wallace. "A Framework for Automatic Causality Extraction Using Semantic Similarity." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35193.

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Textual documents are the most common way of storing and distributing information within organizations. Extracting useful information from large text collections is therefore the goal of every organization that would like to take advantage of the experience encapsulated in those texts. Entering data using a free text style is easy, as it does not require any special training. However, unstructured texts pose a major challenge for automatic extraction and retrieval systems. Generally, deep levels of text analysis using advanced and complex linguistic processing are necessary that involve comput

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Harmsen, Wieke, Catia Cucchiarini, and Helmer Strik. "AUTOMATIC QUANTITATIVE ANALYSIS OF SPELLING ERRORS IN TEXTS WRITTEN BY SIXTH GRADERS." In 13th International Conference on Education and New Learning Technologies. IATED, 2021. http://dx.doi.org/10.21125/edulearn.2021.1798.

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Bolea, Speranta Cecilia. "Implementation of an Algorithm for Automatic Segmentation of Texts based on Stylometric Analysis." In 2021 7th International Symposium on Electrical and Electronics Engineering (ISEEE). IEEE, 2021. http://dx.doi.org/10.1109/iseee53383.2021.9628868.

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Suzen, Neslihan, Alexander N. Gorban, Jeremy Levesley, and Evgeny M. Mirkes. "An Informational Space based Semantic Analysis for Scientific Texts." In 10th International Conference on Foundations of Computer Science & Technology (FCST 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120807.

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One major problem in Natural Language Processing is the automatic analysis and representation of human language. Human language is ambiguous and deeper understanding of semantics and creating human-to-machine interaction have required an effort in creating the schemes for act of communication and building common-sense knowledge bases for the ‘meaning’ in texts. This paper introduces computational methods for semantic analysis and the quantifying the meaning of short scientific texts. Computational methods extracting semantic feature are used to analyse the relations between texts of messages a

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Reports on the topic "Automatic analysis of texts"

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Griffiths, Rachael M. Handwritten Text Recognition (HTR) for Tibetan Manuscripts in Cursive Script. Verlag der Österreichischen Akademie der Wissenschaften, 2024. http://dx.doi.org/10.1553/tibschol_erc_htr.

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The use of advanced computational methods for the analysis of digitised texts is becoming increasingly popular in humanities and social science research. One such technology is Handwritten Text Recognition (HTR), which generates transcripts from digitised texts with machine learning approaches, to enable full-text search and analysis. Up to now, HTR models for Tibetan manuscripts in cursive script have not been available. This paper introduces work carried out as part of the The Dawn of Tibetan Buddhist Scholasticism (11th-13th) TibSchol) project at the Austrian Academy of Sciences, which is u

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Zelenskyi, Arkadii A. Relevance of research of programs for semantic analysis of texts and review of methods of their realization. [б. в.], 2018. http://dx.doi.org/10.31812/123456789/2884.

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One of the main tasks of applied linguistics is the solution of the problem of high-quality automated processing of natural language. The most popular methods for processing natural-language text responses for the purpose of extraction and representation of semantics should be systems that are based on the efficient combination of linguistic analysis technologies and analysis methods. Among the existing methods for analyzing text data, a valid method is used by the method using a vector model. Another effective and relevant means of extracting semantics from the text and its representation is

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Marra de Artiñano, Ignacio, Franco Riottini Depetris, and Christian Volpe Martincus. Automatic Product Classification in International Trade: Machine Learning and Large Language Models. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005012.

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Accurately classifying products is essential in international trade. Virtually all countries categorize products into tariff lines using the Harmonized System (HS) nomenclature for both statistical and duty collection purposes. In this paper, we apply and assess several different algorithms to automatically classify products based on text descriptions. To do so, we use agricultural product descriptions from several public agencies, including customs authorities and the United States Department of Agriculture (USDA). We find that while traditional machine learning (ML) models tend to perform we

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Cavin, Edward. A qualitative analysis of nine nonverbal communications texts. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.3238.

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Bickford, Justin R. Automatic Stripe Analysis Tool. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada585203.

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Wendelberger, James G., Juan Duque, Kimberly Ann Kaufeld, and Elizabeth J. Kelly. Automatic Image Analysis Status Report. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1412838.

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Kolgatina, Larisa, Lyudmyla Bilousova, and Oleksandr Kolgatin. Pedagogical diagnostics with use of computer technologies. CEUR-WS, 2013. http://dx.doi.org/10.31812/123456789/3222.

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The technology of the automated pedagogical diagnostics is analysed. The testing strategy, oriented for pedagogical diagnostics purpose, and grading algorithm, which corresponds to Ukrainian school grading standards, are suggested. "Expert 3.05”software for automated pedagogical testing is designed. The methods of administration of the database of the test items are proposed. Some tests on the mathematical topics are prepared with "Expert 3.05". The approbation of these tests in the educational process of Kharkov National Pedagogical University named after G.S.Skovoroda is analysed.

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Buscaino, B. Trend Analysis in Biomedical Texts via Vector Space Model Synonym Extraction. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1165794.

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Dodele, Kathryn. Approaches to composition in selected directing texts: a structural analysis and assessment. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.2467.

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Müller, Andreas. Mastering Qualitative Data Analysis with MAXQDA. Instats Inc., 2025. https://doi.org/10.61700/vwpt4rsa9bh1d1855.

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This seminar provides intensive training on qualitative data analysis using MAXQDA, equipping researchers with skills to manage and interpret data from interviews, focus groups, texts, and multimedia content. Participants will explore foundational and advanced analytical techniques, including Content Analysis and Grounded Theory, to enhance their research projects across various academic disciplines.

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