Academic literature on the topic 'Text linguistics'
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Journal articles on the topic "Text linguistics"
Zikrillaev, Gani Nasrullaevich, and Erkin Boltaevich Jumaev. "INTERPRETATION OF TEXT AND QUESTIONS REL TION OF TEXT AND QUESTIONS RELATED TO THIS MATTER IN FOREIGN LINGUISTICS." Scientific Reports of Bukhara State University 3, no. 3 (March 30, 2019): 79–88. http://dx.doi.org/10.52297/2181-1466/2019/3/3/8.
Full textBobokalonov, Ramazon Radjabovich, and Polatshoh Ramazonovich Bobokalonov. "TEXT LINGUISTICS AND THE PROBLEM OF THE SYNTAX." Scientific Reports of Bukhara State University 5, no. 5 (December 30, 2021): 21–33. http://dx.doi.org/10.52297/2181-1466/2021/5/5/2.
Full textMukhamejanova, G., and A. Mukhamejanova. "POETIC FEATURES OF THE LITERARY TEXT." BULLETIN Series of Philological Sciences 73, no. 3 (July 15, 2020): 123–32. http://dx.doi.org/10.51889/2020-3.1728-7804.19.
Full textYelchibekov, B., A. Rauandina, and B. Yelikbaiev. "FORMATION OF TEXT LINGUISTICS IN THE FIELD OF LINGUISTICS." BULLETIN Series of Philological Sciences 73, no. 3 (July 15, 2020): 36–41. http://dx.doi.org/10.51889/2020-3.1728-7804.06.
Full textTeich, Elke. "System-oriented and text-oriented comparative linguistic research." Languages in Contrast 2, no. 2 (December 31, 1999): 187–210. http://dx.doi.org/10.1075/lic.2.2.04tei.
Full textZhuchkova, Irina. "THESAURUS MODELLING OF THE “TEXT LINGUISTICS” TERMINOLOGICAL FIELD." Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 2. Jazykoznanije, no. 2 (June 2014): 53–59. http://dx.doi.org/10.15688/jvolsu2.2014.2.7.
Full textShokhrukh, Juraev B., and Shaymardanov H. Abror. "THE DEVELOPMENT TENDENCIES OF COMPUTATIONAL LINGUISTICS IN UZBEKISTAN: NLP, MACHINE TRANSLATION, CORPUS LINGUISTICS AND AUTOMATIC TEXT EDITING." American Journal of Social Science and Education Innovations 04, no. 10 (October 1, 2022): 01–05. http://dx.doi.org/10.37547/tajssei/volume04issue10-01.
Full textPushina, N. I., and E. A. Shirokikh. "ECOTEXT IN TEXT LINGUISTICS." Bulletin of Udmurt University. Series History and Philology 32, no. 4 (August 26, 2022): 742–51. http://dx.doi.org/10.35634/2412-9534-2022-32-4-742-751.
Full textCouto, Javier, and Jean-Luc Minel. "Text Linguistics and Navigation." New Approaches in Text Linguistics 23 (September 25, 2009): 91–102. http://dx.doi.org/10.1075/bjl.23.08cou.
Full textBülow-Møller, Anne Marie. "Text Linguistics at Work." Moderna Språk 86, no. 1 (June 1, 1992): 11–16. http://dx.doi.org/10.58221/mosp.v86i1.10255.
Full textDissertations / Theses on the topic "Text linguistics"
Atwell, Eric Steven. "Corpus linguistics and language learning : bootstrapping linguistic knowledge and resources from text." Thesis, University of Leeds, 2008. http://etheses.whiterose.ac.uk/7504/.
Full textClough, Paul D. "Measuring text reuse." Thesis, University of Sheffield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275023.
Full textBoer, Maria Ângela de Sousa. "Systemic linguistics and the grammar of the text." reponame:Repositório Institucional da UFPR, 2010. http://hdl.handle.net/1884/24322.
Full textTagg, Caroline. "A corpus linguistics study of SMS text messaging." Thesis, University of Birmingham, 2009. http://etheses.bham.ac.uk//id/eprint/253/.
Full textRoloff, Vera Lucia Posnik. "Foreign language reading comprehension: Text representation and the effects of text explicitness and reading ability." Thesis, University of Ottawa (Canada), 1999. http://hdl.handle.net/10393/8791.
Full textMaisto, Alessandro. "A Hybrid Framework for Text Analysis." Doctoral thesis, Universita degli studi di Salerno, 2017. http://hdl.handle.net/10556/2481.
Full textIn Computational Linguistics there is an essential dichotomy between Linguists and Computer Scientists. The rst ones, with a strong knowledge of language structures, have not engineering skills. The second ones, contrariwise, expert in computer and mathematics skills, do not assign values to basic mechanisms and structures of language. Moreover, this discrepancy, especially in the last decades, has increased due to the growth of computational resources and to the gradual computerization of the world; the use of Machine Learning technologies in Arti cial Intelligence problems solving, which allows for example the machines to learn , starting from manually generated examples, has been more and more often used in Computational Linguistics in order to overcome the obstacle represented by language structures and its formal representation. The dichotomy has resulted in the birth of two main approaches to Computational Linguistics that respectively prefers: rule-based methods, that try to imitate the way in which man uses and understands the language, reproducing syntactic structures on which the understanding process is based on, building lexical resources as electronic dictionaries, taxonomies or ontologies; statistic-based methods that, conversely, treat language as a group of elements, quantifying words in a mathematical way and trying to extract information without identifying syntactic structures or, in some algorithms, trying to confer to the machine the ability to learn these structures. One of the main problems is the lack of communication between these two di erent approaches, due to substantial di erences characterizing them: on the one hand there is a strong focus on how language works and on language characteristics, there is a tendency to analytical and manual work. From other hand, engineering perspective nds in language an obstacle, and recognizes in the algorithms the fastest way to overcome this problem. However, the lack of communication is not only an incompatibility: following Harris, the best way to approach natural language, could result by taking the best of both. At the moment, there is a large number of open-source tools that perform text analysis and Natural Language Processing. A great part of these tools are based on statistical models and consist on separated modules which could be combined in order to create a pipeline for the processing of the text. Many of these resources consist in code packages which have not a GUI (Graphical User Interface) and they result impossible to use for users without programming skills. Furthermore, the vast majority of these open-source tools support only English language and, when Italian language is included, the performances of the tools decrease signi cantly. On the other hand, open source tools for Italian language are very few. In this work we want to ll this gap by present a new hybrid framework for the analysis of Italian texts. It must not be intended as a commercial tool, but the purpose for which it was built is to help linguists and other scholars to perform rapid text analysis and to produce linguistic data. The framework, that performs both statistical and rule-based analysis, is called LG-Starship. The idea is to built a modular software that includes, in the beginning, the basic algorithms to perform di erent kind of analysis. Modules will perform the following tasks: Preprocessing Module: a module with which it is possible to charge a text, normalize it or delete stop-words. As output, the module presents the list of tokens and letters which compose the texts with respective occurrences count and the processed text. Mr. Ling Module: a module with which POS tagging and Lemmatization are performed. The module also returns the table of lemmas with the count of occurrences and the table with the quanti cation of grammatical tags. Statistic Module: with which it is possible to calculate Term Frequency and TF-IDF of tokens or lemmas, extract bi-grams and tri-grams units and export results as tables. Semantic Module: which use The Hyperspace Analogue to Language algorithm to calculate semantic similarity between words. The module returns similarity matrices of words per word which can be exported and analyzed. SyntacticModule: which analyze syntax structures of a selected sentence and tag the verbs and its arguments with semantic labels. The objective of the Framework is to build an all-in-one platform for NLP which allows any kind of users to perform basic and advanced text analysis. With the purpose of make the Framework accessible to users who have not speci c computer science and programming language skills, the modules have been provided with an intuitive GUI. The framework can be considered hybrid in a double sense: as explained in the previous lines, it uses both statistical and rule/based methods, by relying on standard statistical algorithms or techniques, and, at the same time, on Lexicon-Grammar syntactic theory. In addition, it has been written in both Java and Python programming languages. LG-Starship Framework has a simple Graphic User Interface but will be also released as separated modules which may be included in any NLP pipelines independently. There are many resources of this kind, but the large majority works for English. There are very few free resources for Italian language and this work tries to cover this need by proposing a tool which can be used both by linguists or other scientist interested in language and text analysis who have no idea about programming languages, as by computer scientists, who can use free modules in their own code or in combination with di erent NLP algorithms. The Framework takes the start from a text or corpus written directly by the user or charged from an external resource. The LG-Starship Framework work ow is described in the owchart shown in g. 1. The pipeline shows that the Pre-Processing Module is applied on original imported or generated text in order to produce a clean and normalized preprocessed text. This module includes a function for text splitting, a stop-word list and a tokenization method. On the text preprocessed the Statistic Module or the Mr. Ling Module can be applied. The rst one, which includes basic statistics algorithm as Term Frequency, tf-idf and n-grams extraction, produces as output databases of lexical and numerical data which can be used to produce charts or perform more external analysis; the second one, is divided in two main task: a Pos tagger, based on the Averaged Perceptron Tagger [?] and trained on the Paisà Corpus [Lyding et al., 2014], perform the Part-Of- Speech Tagging and produce an annotated text. A lemmatization method, which relies on a set of electronic dictionaries developed at the University of Salerno [Elia, 1995, Elia et al., 2010], take as input the Postagged text and produces a new lemmatized version of original text with information about syntactic and semantic properties. This lemmatized text, which can also be processed with the Statistic Module, serves as input for two deeper level of text analysis carried out by both the Syntactic Module and the Semantic Module. The rst one lays on the Lexicon Grammar Theory [Gross, 1971, 1975] and use a database of Predicate structures in development at the Department of Political, Social and Communication Science. Its objective is to produce a Dependency Graph of the sentences that compose the text. The Semantic Module uses the Hyperspace Analogue to Language distributional semantics algorithm [Lund and Burgess, 1996] trained on the Paisà Corpus to produce a semantic network of the words of the text. These work ow has been included in two di erent experiments in which two User Generated Corpora have been involved. The rst experiment represent a statistical study of the language of Rap Music in Italy through the analysis of a great corpus of Rap Song lyrics downloaded from on line databases of user generated lyrics. The second experiment is a Feature-Based Sentiment Analysis project performed on user product reviews. For this project we integrated a large domain database of linguistic resources for Sentiment Analysis, developed in the past years by the Department of Political, Social and Communication Science of the University of Salerno, which consists of polarized dictionaries of Verbs, Adjectives, Adverbs and Nouns. These two experiment underline how the linguistic framework can be applied to di erent level of analysis and to produce both Qualitative data and Quantitative data. For what concern the obtained results, the Framework, which is only at a Beta Version, obtain discrete results both in terms of processing time that in terms of precision. Nevertheless, the work is far from being considered complete. More algorithms will be added to the Statistic Module and the Syntactic Module will be completed. The GUI will be improved and made more attractive and modern and, in addiction, an open-source on-line version of the modules will be published. [edited by author]
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Kof, Leonid. "Text analysis for requirements engineering : application of computational linguistics /." Saarbrücken : VDM Verl. Dr. Müller, 2007. http://deposit.d-nb.de/cgi-bin/dokserv?id=3021639&prov=M&dok_var=1&dok_ext=htm.
Full textDawson, David Allan. "Text-linguistics and Biblical Hebrew : an examination of methodologies." Thesis, University of Edinburgh, 1994. http://hdl.handle.net/1842/19674.
Full textFulford, Heather. "Term acquisition : a text-probing approach." Thesis, University of Surrey, 1997. http://epubs.surrey.ac.uk/843700/.
Full textLaffling, John D. "Machine disambiguation and translation of polysemous nouns : a lexicon-driven model for text-semantic analysis and parallel text-dependent transfer in German-English translation of party political texts." Thesis, University of Wolverhampton, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.254466.
Full textBooks on the topic "Text linguistics"
Giuffrè, Mauro. Text Linguistics and Classical Studies. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47931-6.
Full textText-linguistics and biblical Hebrew. Sheffield, England: Sheffield Academic Press, 1994.
Find full textEsser, Jürgen. Introduction to English text-linguistics. Frankfurt am Main: Peter Lang, 2009.
Find full textEsser, Jürgen. Introduction to English text-linguistics. Frankfurt am Main: Peter Lang, 2009.
Find full textEsser, Jürgen. Introduction to English text-linguistics. Frankfurt am Main: Peter Lang, 2009.
Find full textEsser, Jürgen. Introduction to English text-linguistics. Frankfurt am Main: Peter Lang, 2009.
Find full text1935-, Ghadessy Mohsen, ed. Text and context in functional linguistics. Amsterdam: J. Benjamins, 1999.
Find full textGelbukh, Alexander, ed. Computational Linguistics and Intelligent Text Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12116-6.
Full textGelbukh, Alexander, ed. Computational Linguistics and Intelligent Text Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44686-9.
Full textGhadessy, Mohsen, ed. Text and Context in Functional Linguistics. Amsterdam: John Benjamins Publishing Company, 1999. http://dx.doi.org/10.1075/cilt.169.
Full textBook chapters on the topic "Text linguistics"
de Beaugrande, Robert. "Text linguistics." In Handbook of Pragmatics, 1394–403. Amsterdam: John Benjamins Publishing Company, 2022. http://dx.doi.org/10.1075/hop.m2.tex1.
Full textChilton, Paul. "Text Linguistics." In English Language, 170–85. London: Macmillan Education UK, 2009. http://dx.doi.org/10.1007/978-1-137-07789-9_9.
Full textChilton, Paul, and Christopher Hart. "Text Linguistics." In English Language, 119–33. London: Macmillan Education UK, 2018. http://dx.doi.org/10.1057/978-1-137-57185-4_8.
Full textde Beaugrande, Robert. "Text linguistics." In Handbook of Pragmatics, 536–44. Amsterdam: John Benjamins Publishing Company, 1995. http://dx.doi.org/10.1075/hop.m.tex1.
Full textde Beaugrande, Robert. "Text linguistics." In Discursive Pragmatics, 286–96. Amsterdam: John Benjamins Publishing Company, 2011. http://dx.doi.org/10.1075/hoph.8.16deb.
Full textAbdul-Raof, Hussein. "Text linguistics." In Text Linguistics of Qur’anic Discourse, 9–27. London ; New York, NY : Routledge, 2018. | Series: Culture and civilization in the Middle East ; 59: Routledge, 2018. http://dx.doi.org/10.4324/9781315670942-2.
Full textKohnen, Thomas. "Historical text linguistics." In English Historical Linguistics 2008, 165–88. Amsterdam: John Benjamins Publishing Company, 2012. http://dx.doi.org/10.1075/cilt.324.10koh.
Full textÖstman, Jan-Ola, and Tuija Virtanen. "Text and discourse linguistics." In Handbook of Pragmatics, 1376–93. Amsterdam: John Benjamins Publishing Company, 2022. http://dx.doi.org/10.1075/hop.m2.tex4.
Full textPérez-Paredes, Pascual. "Analysing text." In Corpus Linguistics for Education, 20–34. Abingdon, Oxon; New York, NY: Routledge, 2020. | Series: Routledge corpus linguistics guides: Routledge, 2020. http://dx.doi.org/10.4324/9780429243615-2.
Full textÖstman, Jan-Ola, and Tuija Virtanen. "Text and discourse linguistics." In Handbook of Pragmatics, 1–24. Amsterdam: John Benjamins Publishing Company, 2012. http://dx.doi.org/10.1075/hop.16.tex4.
Full textConference papers on the topic "Text linguistics"
Ryabchenko, Natalia A. "Innovative Approaches In Linguistics: Network Analysis Of Linguistic Data." In X International Conference “Word, Utterance, Text: Cognitive, Pragmatic and Cultural Aspects”. European Publisher, 2020. http://dx.doi.org/10.15405/epsbs.2020.08.138.
Full textALIC, Liliana. "LA RECENSION DE TEXTE ET LE TEXTE SCIENTIFIQUE: UN AIR DE FAMILLE? IS THERE A FAMILY RESEMBLANCE BETWEEN BOOK REVIEW AND SCIENTIFIC TEXT?" In Synergies in Communication. Editura ASE, 2022. http://dx.doi.org/10.24818/sic/2021/01.02.
Full textFenogenova, Alena. "Text Simplification with Autoregressive Models." In Computational Linguistics and Intellectual Technologies. Russian State University for the Humanities, 2021. http://dx.doi.org/10.28995/2075-7182-2021-20-227-234.
Full textPugachev, Leonid, and Mikhail Burtsev. "Short Text Clustering with Transformers." In Computational Linguistics and Intellectual Technologies. Russian State University for the Humanities, 2021. http://dx.doi.org/10.28995/2075-7182-2021-20-571-577.
Full textZimmerling, Anton. "Historical Text Corpora and the Conclusiveness of Linguistic Analysis." In Dialogue. RSUH, 2022. http://dx.doi.org/10.28995/2075-7182-2022-21-586-593.
Full textPonomareva, Natalia, Jasmijn Bastings, and Sergei Vassilvitskii. "Training Text-to-Text Transformers with Privacy Guarantees." In Findings of the Association for Computational Linguistics: ACL 2022. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.findings-acl.171.
Full textGuo, Mandy, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, and Yinfei Yang. "LongT5: Efficient Text-To-Text Transformer for Long Sequences." In Findings of the Association for Computational Linguistics: NAACL 2022. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.findings-naacl.55.
Full textYue, Xiang, Minxin Du, Tianhao Wang, Yaliang Li, Huan Sun, and Sherman S. M. Chow. "Differential Privacy for Text Analytics via Natural Text Sanitization." In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.findings-acl.337.
Full textBakhteev, Oleg, Rita Kuznetsova, Andrey Khazov, Aleksandr Ogaltsov, Kamil Safin, Tatyana Gorlenko, Marina Suvorova, et al. "Near-duplicate handwritten document detection without text recognition." In Computational Linguistics and Intellectual Technologies. Russian State University for the Humanities, 2021. http://dx.doi.org/10.28995/2075-7182-2021-20-47-57.
Full textLi, Tongliang, Lei Fang, Jian-Guang Lou, and Zhoujun Li. "TWT: Table with Written Text for Controlled Data-to-Text Generation." In Findings of the Association for Computational Linguistics: EMNLP 2021. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.findings-emnlp.107.
Full textReports on the topic "Text linguistics"
Zelenskyi, Arkadii A. Relevance of research of programs for semantic analysis of texts and review of methods of their realization. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2884.
Full textMakhachashvili, Rusudan K., Svetlana I. Kovpik, Anna O. Bakhtina, and Ekaterina O. Shmeltser. Technology of presentation of literature on the Emoji Maker platform: pedagogical function of graphic mimesis. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3864.
Full textBilovska, Natalia. HYPERTEXT: SYNTHESIS OF DISCRETE AND CONTINUOUS MEDIA MESSAGE. Ivan Franko National University of Lviv, March 2021. http://dx.doi.org/10.30970/vjo.2021.50.11104.
Full textKNYAZEVA, V., A. BILYALOVA, and E. IBRAGIMOVA. INTERTEXT AS A LEXICAL AND SEMANTIC TOOL OF SUGGESTION. Science and Innovation Center Publishing House, 2022. http://dx.doi.org/10.12731/2077-1770-2022-14-2-3-39-49.
Full textPALIY, T., and A. BAGIYAN. CHARACTERISTIC OF A TEACHER-PHILOLOGIST’S PROFESSIONAL PERSONALITY THROUGH THE PRISM OF AXIOLOGY. Science and Innovation Center Publishing House, 2021. http://dx.doi.org/10.12731/2658-4034-2021-12-4-2-48-58.
Full textBilovska, Natalia. TACTICS OF APPROACHING THE AUTHOR CLOSER TO THE READER: INTERACTIVE COOPERATION. Ivan Franko National University of Lviv, February 2022. http://dx.doi.org/10.30970/vjo.2022.51.11408.
Full textMajchrowska, Justyna. TESTIMONIAL IN (NEW) MEDIA. Ivan Franko National University of Lviv, March 2021. http://dx.doi.org/10.30970/vjo.2021.50.11109.
Full textKankash, Н., Т. Cherkasova, S. Novoseletska, N. Shapran, and L. Bilokonenko. The Use of Linguistic Means of Figurativeness and Evaluativity to Exert Influence in the Speeches of the Chief Delegates of the Ukrainian SSR at the Sessions of the UN General Assembly. Криворізький державний педагогічний університет, 2020. http://dx.doi.org/10.31812/123456789/4648.
Full textYatsymirska, Mariya. MODERN MEDIA TEXT: POLITICAL NARRATIVES, MEANINGS AND SENSES, EMOTIONAL MARKERS. Ivan Franko National University of Lviv, February 2022. http://dx.doi.org/10.30970/vjo.2022.51.11411.
Full textБережна, Маргарита Василівна. Translator’s Gender in the Target Text. Publishing House “Baltija Publishing”, 2020. http://dx.doi.org/10.31812/123456789/4140.
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