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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 Hits 1 – 20 of 47 1 Deep learning approaches to text production Narayan, Shashi; Gardent, Claire. - [San Rafael, California] : Morgan & Claypool Publishers, 2020 Leibniz-Zentrum Allgemeine Sprachwissenschaft Show details 2 Hands-on natural language processing with PyTorch 1.x : build smart, AI-driven linguistic applications using deep learning and NLP techniques Dop, Thomas. - Mumbai : Packt, 2020 BLLDB UB Frankfurt Linguistik Show details 3 Le traitement automatique des langues en question : des machines qui comprennent le français ? Cori, Marcel. - [Paris] : Cassini, 2020 BLLDB UB Frankfurt Linguistik Show details 4 Natural language processing with Spark NLP : learning to understand text at scale Thomas, Alex. - Tokyo : O'Reilly, 2020 BLLDB UB Frankfurt Linguistik Show details 5 Languages in space and time : models and methods from complex systems theory Patriarca, Marco; Léonard, Jean-Léo; Heinsalu, Els. - Cambridge, United Kingdom : Cambridge University Press, 2020 BLLDB UB Frankfurt Linguistik Show details 6 Enriched meanings: natural language semantics with category theory Asudeh, Ash; Giorgolo, Gianluca. - London : Oxford University Press, 2020 IDS Bibliografie zur deutschen Grammatik Show details 7 Reflektierte alggorithmische Textanalyse: interdisziplinäre(s) Arbeiten in der CRETA-Werkstatt Reiter, Nils (Hrsg.); Pichler, Axel (Hrsg.); Kuhn, Jonas (Hrsg.). - Berlin; Boston, Mass. : de Gruyter, 2020 IDS Bibliografie zur deutschen Grammatik Show details 8 Machine translation of user-generated content Lohar, Pintu. - : Dublin City University. School of Computing, 2020. : Dublin City University. ADAPT, 2020 In: Lohar, Pintu (2020) Machine translation of user-generated content. PhD thesis, Dublin City University. (2020) BASE Show details 9 Identifying and Modeling Code-Switched Language Soto Martinez, Victor. - 2020 BASE Show details 10 Essays on the use of computational linguistics in marketing Lemaire, Alain Philippe. - 2020 BASE Show details 11 Theoretical, empirical and computational approaches to agreement with coordination structures ; Les approches théoriques, empiriques et computationnelles pour l’accord avec les structures coordonnées An, Aixiu. - : HAL CCSD, 2020 In: https://tel.archives-ouvertes.fr/tel-03256559 ; Linguistics. Université de Paris, 2020. English. ⟨NNT : 2020UNIP7115⟩ (2020) BASE Show details 12 Machine learning methods for vector-based compositional semantics ... Maillard, Jean. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 13 Learning meaning representations for text generation with deep generative models ... Cao, Kris. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 14 Computer-Assisted Language Comparison in Practice. Tutorials on Computational Approaches to the History and Diversity of Languages. Volume II ... HC User. - : Humanities Commons, 2020 BASE Show details 15 Halbautomatisches Erstellen von Concept Maps : von Christoph Presch ... : Semi-automatic creation of concept maps ... Presch, Christoph. - : TU Wien, 2020 BASE Show details 16 Analyse einer dynamischen Sammlung von Zeitungsartikeln mit inhaltsbasierten Methoden ... : Analysis of a dynamic collection of news articles with content-based methods ... Neumeyer, Markus. - : TU Wien, 2020 BASE Show details 17 JOKE RECOMMENDER SYSTEM USING HUMOR THEORY ... Soumya Agrawal. - : Purdue University Graduate School, 2020 Abstract: The fact that every individual has a different sense of humor and it varies greatly from one person to another means that it is a challenge to learn any individual’s humor preferences. Humor is much more than just a source of entertainment; it is an essential tool that aids communication. Understanding humor preferences can lead to improved social interactions and bridge existing social or economic gaps. In this study, we propose a methodology that aims to develop a recommendation system for jokes by analyzing its text. Various researchers have proposed different theories of humor depending on their area of focus. This exploratory study focuses mainly on Attardo and Raskin’s (1991) General Theory of Verbal Humor and implements the knowledge resources defined by it to annotate the jokes. These annotations contain the characteristics of the jokes and also play an important role in determining how alike these jokes are. We use Lin’s similarity metric (Lin, 1998) to computationally capture this similarity. The ... Keyword: 200402 Computational Linguistics; 80107 Natural Language Processing; Applied Computer Science; FOS Computer and information sciences; FOS Languages and literature; Linguistics URL: https://hammer.figshare.com/articles/JOKE_RECOMMENDER_SYSTEM_USING_HUMOR_THEORY/12735302/1 https://dx.doi.org/10.25394/pgs.12735302.v1 BASE Hide details 18 JOKE RECOMMENDER SYSTEM USING HUMOR THEORY ... Soumya Agrawal. - : Purdue University Graduate School, 2020 BASE Show details 19 Identifying and Modeling Code-Switched Language ... Soto Martinez, Victor. - : Columbia University, 2020 BASE Show details 20 Essays on the use of computational linguistics in marketing ... Lemaire, Alain Philippe. - : Columbia University, 2020 BASE Show details Page: 1 2 3

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