Дисертації з теми "Computational linguistic models"
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Penton, Dave. "Linguistic data models : presentation and representation /." Connect to thesis, 2006. http://eprints.unimelb.edu.au/archive/00002875.
Повний текст джерелаTonkes, Bradley. "On the origins of linguistic structure : computational models of the evolution of language /." St. Lucia, Qld, 2001. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe16529.pdf.
Повний текст джерелаvanCort, Tracy. "Computational Evolutionary Linguistics." Scholarship @ Claremont, 2001. https://scholarship.claremont.edu/hmc_theses/137.
Повний текст джерелаEvans, Owain Rhys. "Bayesian computational models for inferring preferences." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101522.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 130-131).
This thesis is about learning the preferences of humans from observations of their choices. It builds on work in economics and decision theory (e.g. utility theory, revealed preference, utilities over bundles), Machine Learning (inverse reinforcement learning), and cognitive science (theory of mind and inverse planning). Chapter 1 lays the conceptual groundwork for the thesis and introduces key challenges for learning preferences that motivate chapters 2 and 3. I adopt a technical definition of 'preference' that is appropriate for inferring preferences from choices. I consider what class of objects preferences should be defined over. I discuss the distinction between actual preferences and informed preferences and the distinction between basic/intrinsic and derived/instrumental preferences. Chapter 2 focuses on the challenge of human 'suboptimality'. A person's choices are a function of their beliefs and plans, as well as their preferences. If they have inaccurate beliefs or make inefficient plans, then it will generally be more difficult to infer their preferences from choices. It is also more difficult if some of their beliefs might be inaccurate and some of their plans might be inefficient. I develop models for learning the preferences of agents subject to false beliefs and to time inconsistency. I use probabilistic programming to provide a concise, extendable implementation of preference inference for suboptimal agents. Agents performing suboptimal sequential planning are represented as functional programs. Chapter 3 considers how preferences vary under different combinations (or &compositions') of outcomes. I use simple mathematical functional forms to model composition. These forms are standard in microeconomics, where the outcomes in question are quantities of goods or services. These goods may provide the same purpose (and be substitutes for one another). Alternatively, they may combine together to perform some useful function (as with complements). I implement Bayesian inference for learning the preferences of agents making choices between different combinations of goods. I compare this procedure to empirical data for two different applications.
by Owain Rhys Evans.
Ph. D. in Linguistics
Heiberg, Andrea Jeanine. "Features in optimality theory: A computational model." Diss., The University of Arizona, 1999. http://hdl.handle.net/10150/288983.
Повний текст джерелаGwei, G. M. "New models of natural language for consultative computing." Thesis, University of Nottingham, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378986.
Повний текст джерелаClark, Stephen. "Class-based statistical models for lexical knowledge acquisition." Thesis, University of Sussex, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.341541.
Повний текст джерелаBelz, Anja. "Computational learning of finite-state models for natural language processing." Thesis, University of Sussex, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311331.
Повний текст джерелаTang, Haijiang. "Building phrase based language model from large corpus /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202002%20TANG.
Повний текст джерелаIncludes bibliographical references (leaves 74-79). Also available in electronic version. Access restricted to campus users.
Mitchell, Jeffrey John. "Composition in distributional models of semantics." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/4927.
Повний текст джерелаBatchkarov, Miroslav Manov. "Evaluating distributional models of compositional semantics." Thesis, University of Sussex, 2016. http://sro.sussex.ac.uk/id/eprint/61062/.
Повний текст джерелаRoberts, Philip J. "Towards a computer model of the historical phonology and morphology of Latin." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:d3ef315c-3d5c-486b-8fbe-0fa6fdbb8219.
Повний текст джерелаAl-Raheb, Yafa. "Speaker/hearer representation in a discourse representation theory model of presupposition : a computational-linguistic approach." Thesis, University of East Anglia, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426947.
Повний текст джерелаSandelius, Hugo. "Creating Knowledge Graphs using Distributional Semantic Models." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199702.
Повний текст джерелаRitz, Julia. "Discourse-givenness of noun phrases : theoretical and computational models." Phd thesis, Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2014/7081/.
Повний текст джерелаDie vorliegende Arbeit gibt formale Definitionen der Konzepte Diskursgegebenheit, Koreferenz und Referenz. Zudem wird über Experimente berichtet, Nominalphrasen im Deutschen und Englischen hinsichtlich ihrer Diskursgegebenheit zu klassifizieren. Die Definitionen basieren auf Arbeiten von Bach (1987) zu Referenz, Kibble und van Deemter (2000) zu Koreferenz und der Diskursrepräsentationstheorie (Kamp und Reyle, 1993). In den Experimenten wurden die koreferenzannotierten Korpora MUC-7, OntoNotes und ARRAU (Englisch) und TüBa-D/Z (Deutsch) verwendet. Sie umfassen die Klassifikationsalgorithmen J48 (Entscheidungsbäume), Ripper (regelbasiertes Lernen) und lineare Support Vector Machines. Mehrere neue Klassifikationsmerkmale werden vorgeschlagen, die die Spezifizität der Nominalphrase messen, sowie ihren Kontext abbilden. Mit Hilfe dieser Merkmale kann eine signifikante Verbesserung der Klassifikation erreicht werden.
Jonasson, Michael. "Fördomsfulla associationer i en svenskvektorbaserad semantisk modell." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159027.
Повний текст джерелаWord embeddings are a powerful technique where word meaning can be represented by vectors containing actual numbers. The vectors allow geometric operations that capture semantically important relationships between the words. In this study WEAT is applied in order to examine whether statistical properties of words pertaining to bias can be found in a swedish word embedding trained on a corpus from a swedish newspaper. The results shows that the word embedding can represent several of the IAT documented biases that where tested. A second method, WEFAT, is applied to the word embedding in order to explore the embeddings ability to represent actual statistical properties, which is also done successfully. The results from this study lends support to the validity of both methods aswell as illuminating the issue of problematic relationships between words in word embeddings.
Tengstrand, Lisa. "Abbreviation Expansion in Swedish Clinical Text : Using Distributional Semantic Models and Levenshtein Distance Normalization." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-226235.
Повний текст джерелаRitz, Julia [Verfasser], and Stefan [Akademischer Betreuer] Evert. "Discourse-givenness of noun phrases : theoretical and computational models [[Elektronische Ressource]] / Julia Ritz. Betreuer: Stefan Evert." Potsdam : Universitätsbibliothek der Universität Potsdam, 2014. http://d-nb.info/1053125704/34.
Повний текст джерелаRitz, Julia Verfasser], and Stefan [Akademischer Betreuer] [Evert. "Discourse-givenness of noun phrases : theoretical and computational models [[Elektronische Ressource]] / Julia Ritz. Betreuer: Stefan Evert." Potsdam : Universitätsbibliothek der Universität Potsdam, 2014. http://d-nb.info/1053125704/34.
Повний текст джерелаKonrad, Karsten. "Model generation for natural language interpretation and analysis /." Berlin [u.a.] : Springer, 2004. http://www.loc.gov/catdir/enhancements/fy0818/2004042936-d.html.
Повний текст джерелаProst, Jean-Philippe. "Modelling Syntactic Gradience with Loose Constraint-based Parsing." Phd thesis, Université de Provence - Aix-Marseille I, 2008. http://tel.archives-ouvertes.fr/tel-00352828.
Повний текст джерелаNous suggérons d'élargir au langage mal formé les concepts de Gradience Intersective et de Gradience Subsective, proposés par Aarts pour la modélisation de jugements graduels. Selon ce nouveau modèle, le problème que soulève la gradience concerne la classification d'un énoncé dans une catégorie particulière, selon des critères basés sur les caractéristiques syntaxiques de l'énoncé. Nous nous attachons à étendre la notion de Gradience Intersective (GI) afin qu'elle concerne le choix de la meilleure solution parmi un ensemble de candidats, et celle de Gradience Subsective (GS) pour qu'elle concerne le calcul du degré de typicité de cette structure au sein de sa catégorie. La GI est alors modélisée à l'aide d'un critère d'optimalité, tandis que la GS est modélisée par le calcul d'un degré d'acceptabilité grammaticale. Quant aux caractéristiques syntaxiques requises pour permettre de classer un énoncé, notre étude de différents cadres de représentation pour la syntaxe du langage naturel montre qu'elles peuvent aisément être représentées dans un cadre de syntaxe modèle-théorique (Model-Theoretic Syntax). Nous optons pour l'utilisation des Grammaires de Propriétés (GP), qui offrent, précisément, la possibilité de modéliser la caractérisation d'un énoncé. Nous présentons ici une solution entièrement automatisée pour la modélisation de la gradience syntaxique, qui procède de la caractérisation d'une phrase bien ou mal formée, de la génération d'un arbre syntaxique optimal, et du calcul d'un degré d'acceptabilité grammaticale pour l'énoncé.
À travers le développement de ce nouveau modèle, la contribution de ce travail comporte trois volets.
Premièrement, nous spécifions un système logique pour les GP qui permet la révision de sa formalisation sous l'angle de la théorie des modèles. Il s'attache notamment à formaliser les mécanismes de satisfaction et de relâche de contraintes mis en oeuvre dans les GP, ainsi que la façon dont ils permettent la projection d'une catégorie lors du processus d'analyse. Ce nouveau système introduit la notion de satisfaction relâchée, et une formulation en logique du premier ordre permettant de raisonner au sujet d'un énoncé.
Deuxièmement, nous présentons notre implantation du processus d'analyse syntaxique relâchée à base de contraintes (Loose Satisfaction Chart Parsing, ou LSCP), dont nous prouvons qu'elle génère toujours une analyse syntaxique complète et optimale. Cette approche est basée sur une technique de programmation dynamique (dynamic programming), ainsi que sur les mécanismes décrits ci-dessus. Bien que d'une complexité élevée, cette solution algorithmique présente des performances suffisantes pour nous permettre d'expérimenter notre modèle de gradience.
Et troisièmement, après avoir postulé que la prédiction de jugements humains d'acceptabilité peut se baser sur des facteurs dérivés de la LSCP, nous présentons un modèle numérique pour l'estimation du degré d'acceptabilité grammaticale d'un énoncé. Nous mesurons une bonne corrélation de ces scores avec des jugements humains d'acceptabilité grammaticale. Qui plus est, notre modèle s'avère obtenir de meilleures performances que celles obtenues par un modèle préexistant que nous utilisons comme référence, et qui, quant à lui, a été expérimenté à l'aide d'analyses syntaxiques générées manuellement.
Schillingmann, Lars [Verfasser]. "A computational model of acoustic packaging / Lars Schillingmann. Technische Fakultät. Research Institute for Cognition and Robotics." Bielefeld : Universitätsbibliothek Bielefeld, Hochschulschriften, 2012. http://d-nb.info/1028427573/34.
Повний текст джерелаWang, Zhen. "Human disease-behavior interactions on complex networks models: incorporating evolutionary game into epidemiology." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/22.
Повний текст джерелаYako, Mary. "Emotional Content in Novels for Literary Genre Prediction : And Impact of Feature Selection on Text Classification Models." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447148.
Повний текст джерелаLin, Jing. "Using a rewriting system to model individual writing styles." Thesis, University of Aberdeen, 2012. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=186641.
Повний текст джерелаBenitez-Quiroz, Carlos Fabian. "A Computational Study of American Sign Language Nonmanuals." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1436909704.
Повний текст джерелаLuo, Ziyang. "Analyzing the Anisotropy Phenomenon in Transformer-based Masked Language Models." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445537.
Повний текст джерелаFaria, Pablo 1978. "Um modelo computacional de aquisição de primeira língua." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/268869.
Повний текст джерелаTese (doutorado) - Universidade Estadual de Campinas, Instituto de Estudos da Linguagem
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Resumo: Neste trabalho, o fenômeno de aquisição de uma língua natural é investigado através de uma modelagem computacional. O aprendiz modelado - apelidado de IASMIM - se caracteriza como um modelo computacional integrado de aquisição de primeira língua, visto que integra os processos de aquisição lexical e sintática. Além disso, o modelo foi concebido de modo a atender certos critérios de plausibilidade empírica e psicológica. A perspectiva teórica que norteia a investigação é a da Gramática Gerativa (cf. Chomsky, 1986) e este é um modelo voltado para a competência linguística, não um modelo de processamento ou de performance (i.e., de uso do conhecimento linguístico). O aprendiz modelado é capaz de adquirir um conhecimento gramatical relativamente abrangente e demonstra algum potencial translinguístico, particularmente no que diz respeito a variações de ordem. As simulações para avaliação do modelo permitem observar a emergência de padrões de adjunção e de recursividade na gramática, considerados aqui como as principais evidências de um conhecimento sintático mais elaborado. Finalmente, o modelo incorpora algumas noções caras à teoria sintática no âmbito do Programa Minimalista (cf. Chomsky, 1995b), tais como set- Merge, pair-Merge, "traço seletor" (cf. Chomsky, 1998), em conjunto com assunções sobre a binariedade das representações sintáticas e a hipótese de que a ordem linear não tem papel na sintaxe (cf. Uriagereka, 1999). O modelo incorpora, ainda, uma versão da representação semântico-conceitual proposta em Jackendoff (1990). Nesta modelagem, estas noções e assunções ganham uma interpretação concreta e integrada, interagindo na determinação das propriedades do conhecimento adquirido
Abstract: In the present work, the acquisition of natural languages is investigated through a computer simulation. The modelled learner - dubbed IASMIM - is characterized as an integrated computational model of first language acquisition, in the sense that it integrates the processes of lexical and syntactic acquisition. Furthermore, the model was conceived in order to be empirically and psychologically plausible. The theoretical perspective of this enterprise is that of Generative Grammar (cf. Chomsky, 1986) and this is a model concerned with linguistic competence, rather than language processing or performance (i.e., how the acquired knowledge is put to use). The modelled learner is capable of acquiring a relatively broad grammatical knowledge and shows some crosslinguistic abilities, in particular, the ability to handle languages with distinct word orders. In the simulations for evaluation of the model we can observe the emergence of adjunction and recursive patterns in the grammar, taken here as the main pieces of evidence of a more elaborated syntactic knowledge. Finally, the model embodies some central notions for syntactic theory under the Minimalist Program (cf. Chomsky, 1995b), such as set-Merge, pair-Merge and "selector feature" (cf. Chomsky, 1998), together with the assumptions that syntactic representations are strictly binary branching and that linear word order has no significant role in syntactic phenomena (cf. Uriagereka, 1999). The model also embodies a version of the semantic-conceptual representation proposed in Jackendoff (1990). They take a concrete and integrated existence in this model, interacting with one another to determine the properties of the acquired grammatical knowledge
Doutorado
Linguistica
Doutor em Linguística
Xie, Danke. "A computational biologically-plausible model of working memory for serial order, repetition and binding." Diss., [La Jolla, Calif.] : University of California, San Diego, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p3344748.
Повний текст джерелаTitle from first page of PDF file (viewed April 1, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 150-163).
Stevan, Ostrogonac. "Modeli srpskog jezika i njihova primena u govornim i jezičkim tehnologijama." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2018. https://www.cris.uns.ac.rs/record.jsf?recordId=107812&source=NDLTD&language=en.
Повний текст джерелаA statistical language model, in theory, represents a probability distribution over sequences of words of a language. In practice, it is a tool for estimating probabilities of word sequences of interest. Mathematical basis related to language models is mostly language independent. However, the quality of trained models depends not only on training algorithms, but on the amount and quality of available training data as well. For languages with complex morphology, such as Serbian, textual corpora for training language models need to be significantly larger than the corpora needed for training language models for languages with relatively simple morphology, such as English. This research represents the entire process of developing language models for Serbian, starting with collecting and preprocessing of textual contents, extending to adaptation of algorithms and development of methods for addressing the problem of insufficient training data, and finally to adaptation and application of the models in different technologies, such as text-to-speech synthesis, automatic speech recognition, automatic detection and correction of grammar and semantic errors in texts, and determining basics for the application of the models in automatic document classification and other tasks. The core of the development of language models for Serbian is defining morphologic classes of words, based on the information contained within the morphologic dictionary of Serbian, which was one of the results of a previous research.
Faria, Pablo 1978. "Propriedades das línguas naturais e o processo de aquisição = reflexões a partir da implementação do modelo em Berwick (1985)." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/271190.
Повний текст джерелаDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Estudos da Linguagem
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Resumo: Nesta dissertação de mestrado, o objetivo principal é refletir sobre algumas propriedades da linguagem e do processo de aquisição, tomando como ponto de partida questões que surgiram durante o processo de implementação do modelo proposto em Berwick (1985). O quadro teórico geral em que esta pesquisa se situa é o da Gramática Gerativa - na linha chomskiana - e, em particular, o modelo implementado aqui tem como principal base teórica a Gramática Transformacional (Cf. CHOMSKY, 1965). Entre as propriedades da linguagem que discutimos estão: os traços distintivos dos itens lexicais, a assimetria entre especificadores e complementos, categorias vazias e o papel da informação temática na sintaxe. A idéia subjacente que permeia as reflexões é a busca por um olhar mais abstrato sobre o conhecimento gramatical, procurando rever ou até eliminar dispositivos que, em primeiro lugar, aparecem como obstáculos significativos para o analisador e, em segundo lugar, resistem à identificação de evidências para sua aquisição, do ponto de vista do aprendiz da língua. Para atingir estes objetivos, a primeira metade do trabalho faz uma breve discussão teórica, para em seguida trazer uma apresentação razoavelmente detalhada do modelo de Berwick, incluindo exemplos de funcionamento. A segunda metade inclui a discussão dos resultados juntamente com as reflexões sobre a linguagem, procurando apontar caminhos que não apenas possam tornar o modelo de aquisição mais robusto, mas que levantem questões para investigação em teoria gramatical. Neste sentido, este trabalho - por se situar numa área multidisciplinar, a saber, a lingüística computacional - procura tornar explícitas as contribuições que esse tipo de investigação pode fazer à teoria lingüística.
Abstract: The main goal of this MA thesis is to discuss some properties of language and its acquisition process, taking as a starting point some issues that emerged during the implementation of the acquisition model proposed by Berwick (1985). Our general theoretical framework is the Generative Grammar - as proposed in Chomsky's works - and, in particular, the Transformational Grammar model (Cf. CHOMSKY, 1965). Some of the language properties discussed here involve: the set of distinctive features for lexical items, the asymmetry between specifiers and complements, empty categories and the role of thematic information in syntax. The subjacent idea surrounding the reections on language is the search for a more abstract view of the grammatical knowledge such that some of the theoretical devices can be revised or even abandoned. Two main goals drive this effort: first, the elimination of some significant obstacles in the parser's task to analyze sentences. Second, the elimination of those devices for which we cannot find proper evidence for their acquisition, considering the learner's perspective. In order to pursue these goals, in the first part of this work we set out the theoretical background for the whole discussion and give a somewhat detailed presentation of Berwick's acquisition model and examples of its functioning. In the second part we include a discussion of the results upon which the reection is built, whereby we point out some ways not only to increase the robustness of the acquisition model, but also to raise questions for further research in grammatical theory. In this sense, this work - which pertains to an interdisciplinary field, i.e, computational linguistics - tries to make explicit the contributions that this kind of investigation can offer to linguistic theory.
Mestrado
Linguistica
Mestre em Linguística
Roos, Daniel. "Evaluation of BERT-like models for small scale ad-hoc information retrieval." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177675.
Повний текст джерелаTang, Hao. "Bidirectional LSTM-CNNs-CRF Models for POS Tagging." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-362823.
Повний текст джерелаKunz, Jenny. "Neural Language Models with Explicit Coreference Decision." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-371827.
Повний текст джерелаLu, Xiaofei. "Hybrid models for Chinese unknown word resolution." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1154631880.
Повний текст джерелаTao, Joakim, and David Thimrén. "Smoothening of Software documentation : comparing a self-made sequence to sequence model to a pre-trained model GPT-2." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178186.
Повний текст джерелаThis thesis was presented on June 22, 2021, the presentation was done online on Microsoft teams.
Asghari, Parastoo. "Ambiguity in Peace Agreements : Cognitive and Computational Models for Processing Syntactic Ambiguity in Israeli-Palestinian Peace Agreements in English." Thesis, Stockholms universitet, Engelska institutionen, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-152824.
Повний текст джерелаMilajevs, 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.
Повний текст джерелаKim, Seungyeon. "Modeling and visualization of version-controlled documents." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39603.
Повний текст джерелаGrefenstette, Edward Thomas. "Category-theoretic quantitative compositional distributional models of natural language semantics." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:d7f9433b-24c0-4fb5-925b-d8b3744b7012.
Повний текст джерелаSundin, Albin. "Word Space Models for Web User Clustering and Page Prefetching." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-82012.
Повний текст джерелаFang, Yimai. "Proposition-based summarization with a coherence-driven incremental model." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/287468.
Повний текст джерелаBuys, Jan Moolman. "Incremental generative models for syntactic and semantic natural language processing." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:a9a7b5cf-3bb1-4e08-b109-de06bf387d1d.
Повний текст джерелаErenmalm, Elsa. "Multilingual Dependency Parsing of Uralic Languages : Parsing with zero-shot transfer and cross-lingual models using geographically proximate, genealogically related, and syntactically similar transfer languages." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-427278.
Повний текст джерелаAndersson, Henrik. "Anchor-based Topic Modeling with Human Interpretable Results." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-168134.
Повний текст джерелаHägglöf, Hillevi, and Lisa Tengstrand. "A Random Indexing Approach to Unsupervised Selectional Preference Induction." Thesis, Stockholms universitet, Avdelningen för datorlingvistik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-59493.
Повний текст джерелаSvensson, Karin, and Johan Blad. "Exploring NMF and LDA Topic Models of Swedish News Articles." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-429250.
Повний текст джерела陸穎剛 and Wing-kong Luk. "Concept space approach for cross-lingual information retrieval." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B30147724.
Повний текст джерелаJohansson, Oskar. "Parafrasidentifiering med maskinklassificerad data : utvärdering av olika metoder." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167039.
Повний текст джерелаZahra, Shorouq. "Targeted Topic Modeling for Levantine Arabic." Thesis, Uppsala universitet, Institutionen för lingvistik och filologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412975.
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