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Comparação de Métodos para Inferência em Linguagem Natural ; Comparison of Methods for Natural Language Inference
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Souza, Rodrigo Aparecido da Silva. - : Biblioteca Digital de Teses e Dissertações da USP, 2020. : Universidade de São Paulo, 2020. : Faculdade de Filosofia, Letras e Ciências Humanas, 2020
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Using Lexical-Semantic Concepts for Fine-Grained Classification in the Embedding Space
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In: Amsler, Michael. Using Lexical-Semantic Concepts for Fine-Grained Classification in the Embedding Space. 2020, University of Zurich, Faculty of Arts. (2020)
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Machine Translation for Professional Translators
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In: Läubli, Samuel. Machine Translation for Professional Translators. 2020, University of Zurich, Faculty of Arts. (2020)
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Temporal Entity Extraction from Historical Texts
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In: Korchagina, Natalia. Temporal Entity Extraction from Historical Texts. 2020, University of Zurich, Faculty of Arts. (2020)
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Unsupervised Multilingual Alignment using Wasserstein Barycenter
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Computational Analysis of Arguments and Persuasive Strategies in Political Discourse
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Design, Development, and Evaluation of Research Tools for Evidence-Based Learning: A Digital Game-Based Spelling Training for German Primary School Children
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Prediction of Alzheimer's disease and semantic dementia from scene description: toward better language and topic generalization
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Automatic Poetry Classification and Chronological Semantic Analysis
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Ontology refinement for improved information retrieval in the biomedical domain
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In: TDX (Tesis Doctorals en Xarxa) (2020)
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Defining distinctiveness: A computational and experimental analysis
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Abstract:
Distinctiveness is a fundamental principle of human memory. However, definitions of distinctiveness have largely remained intuitive and imprecise (Hunt & Worthen, 2006). In Experiments 1 and 2, participants studied critical, distinctive words that were embedded in eight different categorized lists. At test, three different types of lures were presented: distinct related lures, categorical related lures, and unrelated lures. Vector-based representations of word meaning were derived using distributional models of semantics to fit the data. Namely, the Bound Encoding of the Language Environment (Jones & Mewhort, 2007) and Latent Semantic Analysis (Landauer & Dumais, 1997) were employed to derive word meaning from written text. These representations were coupled with an instance-based model of human memory, MINERVA 2 (Hintzman, 1988) to model recognition. The same experimental design as in Experiments 1 and 2 was used in Experiments 3 and 4, where DRM materials replaced the old and new category words, and where LSA and BEAGLE were used to derive the distinctive words, the lures related to those distinctive words, and the subsequent unrelated lures. Lastly, Experiment 5 aimed to formulate a priori predictions for recognition when words were sampled at random. ; February 2021
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Keyword:
computational linguistics; computational modelling; natural language processing; semantic distinctiveness; semantic memory; semantic space models; vector space models; word recognition
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URL: http://hdl.handle.net/1993/35178
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On the integration of linguistic features into statistical and neural machine translation
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In: Vanmassenhove, Eva Odette Jef orcid:0000-0003-1162-820X (2019) On the integration of linguistic features into statistical and neural machine translation. PhD thesis, Dublin City University. (2019)
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