Academic literature on the topic 'Semantics of word-forming base'
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Journal articles on the topic "Semantics of word-forming base"
Zalavina, Tatyana Yu, Ludmila I. Antropova, Liliya S. Polyakova, and Yulia V. Yuzhakova. "SOMATIC PHRASEOLOGICAL UNITS WITH A COMMON NEGATIVE CONNOTATION IN NATIONAL LANGUAGES (BASED ON FRENCH)." Theoretical and Applied Linguistics, no. 2 (2019): 18–27. http://dx.doi.org/10.22250/24107190_2019_5_2_18_27.
Full textDanilina, Natalia I. "COGNITIVE POTENTIAL OF VERBS OF SPEECH (on the Material of the Latin Language)." Вестник Пермского университета. Российская и зарубежная филология 12, no. 3 (2020): 15–23. http://dx.doi.org/10.17072/2073-6681-2020-3-15-23.
Full textCharitonidis, Chariton. "Colour verbs in Modern Greek: A cognitive approach." Word Structure 7, no. 2 (October 2014): 125–52. http://dx.doi.org/10.3366/word.2014.0063.
Full textProkopуeva, Aleksandra Egorovna. "The formation of intransitive verbs from the base of quality processes in Kolyma dialect of the Yukaghir language." Филология: научные исследования, no. 11 (November 2020): 120–28. http://dx.doi.org/10.7256/2454-0749.2020.11.34201.
Full textGreshchuk, Vasyl’. "Slovotvirne hnizdo z vershynoyu Khrystos u movniy kartyni svitu ukrayintsiv." Studia Ucrainica Varsoviensia, no. 8 (August 31, 2020): 11–19. http://dx.doi.org/10.31338/2299-7237suv.8.6.
Full textKoriakowcewa, Elena, Larisa Ratsiburskaya, and Marina Sandakova. "Intensifiers in the Language of the 21st Century: Word-Building, Semantics, Syntagmatics and Dynamics of Evalution." Vestnik Volgogradskogo gosudarstvennogo universiteta. Serija 2. Jazykoznanije, no. 5 (January 2022): 6–19. http://dx.doi.org/10.15688/jvolsu2.2021.5.1.
Full textKOLESOVA, IRINA E. "FORMING WORD CLUSTERS BASED ON VERBS WITH THE SEMANTICS ‘SOUNDING’ IN THE DIALECT OF BORBUSHINO VILLAGE." Cherepovets State University Bulletin 1, no. 106 (2022): 119–26. http://dx.doi.org/10.23859/1994-0637-2022-1-106-10.
Full textLiu, Ling, and Sang-Bing Tsai. "Intelligent Recognition and Teaching of English Fuzzy Texts Based on Fuzzy Computing and Big Data." Wireless Communications and Mobile Computing 2021 (July 10, 2021): 1–10. http://dx.doi.org/10.1155/2021/1170622.
Full textBilousova, Viktoria V., and Raisa V. Kelembet. "COMPOSITIONAL SEMANTICS OF THE COMPLEX LANGUAGE FACTS WITH THE ENGLISH -ING FORMANT (on the material of Ukrainian)." Alfred Nobel University Journal of Philology 2, no. 22 (2021): 122–30. http://dx.doi.org/10.32342/2523-4463-2021-2-22-10.
Full textJanz, Arkadiusz, and Maciej Piasecki. "A Weakly supervised word sense disambiguation for Polish using rich lexical resources." Poznan Studies in Contemporary Linguistics 55, no. 2 (June 26, 2019): 339–65. http://dx.doi.org/10.1515/psicl-2019-0013.
Full textDissertations / Theses on the topic "Semantics of word-forming base"
Sinha, Ravi Som. "Graph-based Centrality Algorithms for Unsupervised Word Sense Disambiguation." Thesis, University of North Texas, 2008. https://digital.library.unt.edu/ark:/67531/metadc9736/.
Full textGrover, Ishaan. "A semantics based computational model for word learning." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120694.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 73-77).
Studies have shown that children's early literacy skills can impact their ability to achieve academic success, attain higher education and secure employment later in life. However, lack of resources and limited access to educational content causes a "knowledge gap" between children that come from different socio-economic backgrounds. To solve this problem, there has been a recent surge in the development of Intelligent Tutoring Systems (ITS) to provide learning benefits to children. However, before providing new content, an ITS must assess a child's existing knowledge. Several studies have shown that children learn new words by forming semantic relationships with words they already know. Human tutors often implicitly use semantics to assess a tutee's word knowledge from partial and noisy data. In this thesis, I present a cognitively inspired model that uses word semantics (semantics-based model) to make inferences about a child's vocabulary from partial information about their existing vocabulary. Using data from a one-to-one learning intervention between a robotic tutor and 59 children, I show that the proposed semantics-based model outperforms (on average) models that do not use word semantics (semantics-free models). A subject level analysis of results reveals that different models perform well for different children, thus motivating the need to combine predictions. To this end, I present two methods to combine predictions from semantics-based and semantics-free models and show that these methods yield better predictions of a child's vocabulary knowledge. Finally, I present an application of the semantics-based model to evaluate if a learning intervention was successful in teaching children new words while enhancing their semantic understanding. More concretely, I show that a personalized word learning intervention with a robotic tutor is better suited to enhance children's vocabulary when compared to a non-personalized intervention. These results motivate the use of semantics-based models to assess children's knowledge and build ITS that maximize children's semantic understanding of words.
"This research was supported by NSF IIP-1717362 and NSF IIS-1523118"--Page 10.
by Ishaan Grover.
S.M.
Burton, Marilyn Elizabeth. "Semantics of glory : a cognitive, corpus-based approach to Hebrew word meaning." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/9573.
Full textSinha, Ravi Som Mihalcea Rada F. "Graph-based centrality algorithms for unsupervised word sense disambiguation." [Denton, Tex.] : University of North Texas, 2008. http://digital.library.unt.edu/permalink/meta-dc-9736.
Full textEsin, Yunus Emre. "Improvement Of Corpus-based Semantic Word Similarity Using Vector Space Model." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610759/index.pdf.
Full textManion, Steve Lawrence. "Unsupervised Knowledge-based Word Sense Disambiguation: Exploration & Evaluation of Semantic Subgraphs." Thesis, University of Canterbury. Department of Mathematics & Statistics, 2014. http://hdl.handle.net/10092/10016.
Full textLilliehöök, Hampus. "Extraction of word senses from bilingual resources using graph-based semantic mirroring." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-91880.
Full textI det här arbetet utvinner vi semantisk information som existerar implicit i tvåspråkig data. Vi samlar indata genom att upprepa proceduren semantisk spegling. Datan representeras som vektorer i en stor vektorrymd. Vi bygger sedan en resurs med synonymkluster genom att applicera K-means-algoritmen på vektorerna. Vi granskar resultatet för hand med hjälp av ordböcker, och mot WordNet, och diskuterar möjligheter och tillämpningar för metoden.
Milajevs, Dmitrijs. "A study of model parameters for scaling up word to sentence similarity tasks in distributional semantics." Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/36225.
Full textIslam, Md Aminul. "Applications of corpus-based semantic similarity and word segmentation to database schema matching." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27256.
Full textStigeborn, Olivia. "Text ranking based on semantic meaning of sentences." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300442.
Full textAtt hitta en lämplig kandidat till kundmatchning är en viktig del av ett konsultföretags arbete. Det tar mycket tid och ansträngning för rekryterare på företaget att läsa eventuellt hundratals CV:n för att hitta en lämplig kandidat. Det finns språkteknologiska metoder för att rangordna CV:n med de mest lämpliga kandidaterna rankade högst. Detta säkerställer att rekryterare endast behöver titta på de topprankade CV:erna och snabbt kan få kandidater ut i fältet. Tidigare forskning har använt metoder som räknar specifika nyckelord i ett CV och är kapabla att avgöra om en kandidat har specifika erfarenheter. Huvudmålet med denna avhandling är att använda den semantiska innebörden av texten iCV:n för att få en djupare förståelse för en kandidats erfarenhetsnivå. Den utvärderar också om modellen kan köras på mobila enheter och om algoritmen kan rangordna CV:n oberoende av om CV:erna är på svenska eller engelska. En algoritm skapades som använder ordinbäddningsmodellen DistilRoBERTa som är kapabel att fånga textens semantiska betydelse. Algoritmen utvärderades genom att generera jobbeskrivningar från CV:n genom att skapa en sammanfattning av varje CV. Körtiden, minnesanvändningen och rankningen som den önskade kandidaten fick dokumenterades och användes för att analysera resultatet. När den kandidat som användes för att generera jobbeskrivningen rankades i topp 10 ansågs klassificeringen vara korrekt. Noggrannheten beräknades med denna metod och en noggrannhet på 68,3 % uppnåddes. Resultaten visar att algoritmen kan rangordna CV:n. Algoritmen kan rangordna både svenska och engelska CV:n med en noggrannhet på 67,7 % för svenska och 74,7 % för engelska. Körtiden var i genomsnitt 578 ms vilket skulle möjliggöra att algoritmen kan köras på mobila enheter men minnesanvändningen var för stor. Sammanfattningsvis kan den semantiska betydelsen av CV:n användas för att rangordna CV:n och ett eventuellt framtida arbete är att kombinera denna metod med en metod som räknar nyckelord för att undersöka hur noggrannheten skulle påverkas.
Books on the topic "Semantics of word-forming base"
Schmid, Annemarie. Mettre à toutes les sauces: Analyse sémantico-syntaxique des lexies complexes à base de "mettre". [Metz]: Centre d'analyse syntaxique, Université de Metz, Faculté des lettres et sciences humaines, 1992.
Find full textPapegaaij, Bart C. Word expert semantics: An interlingual knowledge-based approach. Dordrecht: Foris, 1986.
Find full textBavaeva, Ol'ga. Metaphorical parallels of the neutral nomination "man" in modern English. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1858259.
Full textSokolova, Elena. Onomastic space of monuments of writing of Kievan Rus. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1869553.
Full textDressler, Wolfgang U., and Lavinia Merlini Barbaresi. Pragmatics and Morphology. Edited by Yan Huang. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199697960.013.20.
Full textWord expert semantics: An interlingual knowledge-based approach. Dordrecht: Foris Publications, 1986.
Find full textPapegaaij, Bart C. Word Expert Semantics: An Interlingual Knowledge-Based Approach. De Gruyter, Inc., 2019.
Find full textWord Expert Semantics: An Interlingual Knowledge-Based Approach. Mouton De Gruyter, 1986.
Find full textPapegaaij, Bart C. Word Expert Semantics: An Interlingual Knowledge-Based Approach. Foris Pubns USA, 1986.
Find full textGlanville, Peter John. Words, roots, and patterns. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198792734.003.0002.
Full textBook chapters on the topic "Semantics of word-forming base"
Degemmis, M., P. Lops, and G. Semeraro. "WordNet-Based Word Sense Disambiguation for Learning User Profiles." In Semantics, Web and Mining, 18–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11908678_2.
Full textten Haaf, Fenna, Christopher Claassen, Ruben Eschauzier, Joanne Tjan, Daniël Buijs, Flavius Frasincar, and Kim Schouten. "WEB-SOBA: Word Embeddings-Based Semi-automatic Ontology Building for Aspect-Based Sentiment Classification." In The Semantic Web, 340–55. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77385-4_20.
Full textHristea, Florentina T. "Semantic WordNet-Based Feature Selection." In The Naïve Bayes Model for Unsupervised Word Sense Disambiguation, 17–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33693-5_3.
Full textJagoda, Jakub, and Tomasz Boiński. "Assessing Word Difficulty for Quiz-Like Game." In Semantic Keyword-Based Search on Structured Data Sources, 70–79. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74497-1_7.
Full textSun, Maosong, Shengfen Luo, and Benjamin K. T’sou. "Word Extraction Based on Semantic Constraints in Chinese Word-Formation." In Computational Linguistics and Intelligent Text Processing, 202–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30586-6_20.
Full textZuo, Xin, Huanhuan Hu, Weiming Zhang, and Nenghai Yu. "Text Semantic Steganalysis Based on Word Embedding." In Cloud Computing and Security, 485–95. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00015-8_42.
Full textSha, Yun, Ming Xia, Huina Jiang, and Xiaohua Wang. "Word Semantic Orientation Calculation Algorithm Based on Dynamic Standard Word Set." In Advances in Intelligent and Soft Computing, 125–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28314-7_18.
Full textLi, Changliang, Teng Ma, Yujun Zhou, Jian Cheng, and Bo Xu. "Measuring Word Semantic Similarity Based on Transferred Vectors." In Neural Information Processing, 326–35. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70093-9_34.
Full textLi, Xiaoli, Jimin Liu, and Zhongzhi Shi. "A Document Classifier Based on Word Semantic Association." In PRICAI 2000 Topics in Artificial Intelligence, 824. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44533-1_113.
Full textLiao, Xuanyi, and Guang Cheng. "Analysing the Semantic Change Based on Word Embedding." In Natural Language Understanding and Intelligent Applications, 213–23. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50496-4_18.
Full textConference papers on the topic "Semantics of word-forming base"
Grover, Ishaan, Hae Won Park, and Cynthia Breazeal. "A Semantics-based Model for Predicting Children's Vocabulary." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/188.
Full textMazumder, Sahisnu, and Bing Liu. "Context-aware Path Ranking for Knowledge Base Completion." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/166.
Full textSuzen, 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.
Full textShmelev, A. D. "LANGUAGE-SPECIFIC WORDS IN THE LIGHT OF TRANSLATION: THE RUSSIAN TOSKA." In International Conference on Computational Linguistics and Intellectual Technologies "Dialogue". Russian State University for the Humanities, 2020. http://dx.doi.org/10.28995/2075-7182-2020-19-658-669.
Full textJiang, Xiaoze, Jing Yu, Yajing Sun, Zengchang Qin, Zihao Zhu, Yue Hu, and Qi Wu. "DAM: Deliberation, Abandon and Memory Networks for Generating Detailed and Non-repetitive Responses in Visual Dialogue." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/96.
Full textMaximova, Olga, and Tatiana Maykova. "PROPER NAMES AS TERMINOLOGY IN SOCIAL SCIENCE." In NORDSCI Conference Proceedings. Saima Consult Ltd, 2021. http://dx.doi.org/10.32008/nordsci2021/b1/v4/20.
Full textDetkova, J., V. Novitskiy, M. Petrova, and V. Selegey. "DIFFERENTIAL SEMANTIC SKETCHES FOR RUSSIAN INTERNET-CORPORA." In International Conference on Computational Linguistics and Intellectual Technologies "Dialogue". Russian State University for the Humanities, 2020. http://dx.doi.org/10.28995/2075-7182-2020-19-211-227.
Full textXie, Ruobing, Xingchi Yuan, Zhiyuan Liu, and Maosong Sun. "Lexical Sememe Prediction via Word Embeddings and Matrix Factorization." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/587.
Full textMeng, Fanqing, Wenpeng Lu, Yuteng Zhang, Ping Jian, Shumin Shi, and Heyan Huang. "QLUT at SemEval-2017 Task 2: Word Similarity Based on Word Embedding and Knowledge Base." In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/s17-2036.
Full textXu, Jin, Yubo Tao, and Hai Lin. "Semantic word cloud generation based on word embeddings." In 2016 IEEE Pacific Visualization Symposium (PacificVis). IEEE, 2016. http://dx.doi.org/10.1109/pacificvis.2016.7465278.
Full textReports on the topic "Semantics of word-forming base"
Pikilnyak, Andrey V., Nadia M. Stetsenko, Volodymyr P. Stetsenko, Tetiana V. Bondarenko, and Halyna V. Tkachuk. Comparative analysis of online dictionaries in the context of the digital transformation of education. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4431.
Full textYatsymirska, Mariya. SOCIAL EXPRESSION IN MULTIMEDIA TEXTS. Ivan Franko National University of Lviv, February 2021. http://dx.doi.org/10.30970/vjo.2021.49.11072.
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