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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 4 Hits 1 – 20 of 70 1 A Study of Commonsense Reasoning with Language Models Branco, Ruben Miguel Rosa. - 2022 BASE Show details 2 Shortcutted Commonsense: Data Spuriousness in Deep Learning of Commonsense Reasoning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Branco, Ruben; Branco, António. - : Underline Science Inc., 2021 BASE Show details 3 Anaphoric Binding: an integrated overview ... Branco, António. - : arXiv, 2021 BASE Show details 4 The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe Rehm, Georg; Marheinecke, Katrin; Hegele, Stefanie... In: Language Resources and Evaluation Conference ; https://hal.archives-ouvertes.fr/hal-02892154 ; Language Resources and Evaluation Conference, ELDA/ELRA, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/en/ (2020) BASE Show details 5 The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ... Rehm, Georg; Marheinecke, Katrin; Hegele, Stefanie. - : arXiv, 2020 BASE Show details 6 The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ... Rehm, Georg; Marheinecke, Katrin; Hegele, Stefanie. - : Zenodo, 2020 BASE Show details 7 The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe ... Rehm, Georg; Marheinecke, Katrin; Hegele, Stefanie. - : Zenodo, 2020 BASE Show details 8 Comparative Probing of Lexical Semantics Theories for Cognitive Plausibility and Technological Usefulness ... Branco, António; Rodrigues, João; Salawa, Małgorzata. - : arXiv, 2020 BASE Show details 9 Comparative Probing of Lexical Semantics Theories for Cognitive Plausibility and Technological Usefulness ... The 28th International Conference on Computational Linguistics 2020; Branco, António; Branco, Ruben. - : Underline Science Inc., 2020 BASE Show details 10 Merging External Bilingual Pairs into Neural Machine Translation ... Wang, Tao; Kuang, Shaohui; Xiong, Deyi. - : arXiv, 2019 BASE Show details 11 Open Resources and Tools for the Shallow Processing of Portuguese: the TagShare project Barreto, Florbela; Branco, António; Ferreira, Eduardo. - : European Language Resources Association, 2019 BASE Show details 12 Portuguese-Chinese neural machine translation Santos, Rodrigo Soares dos. - 2019 BASE Show details 13 Named Entities in the QTLeap Corpus of Online Helpdesk Interactions Pereira, Rita; Carvalho, Rita de; Silva, João. - : Associação Portuguesa de Linguística, 2018 BASE Show details 14 CINTIL DependencyBank PREMIUM. A corpus of grammatical dependencies for Portuguese Carvalho, Rita de; Querido, Andreia; Campos, Marisa. - : European Language Resources Association, 2018 BASE Show details 15 Lexical semantics annotation for enriched Portuguese corpora Neale, Steven; Valadas, Rita; Silva, João. - : Springer, 2018 BASE Show details 16 Replicability and reproducibility of research results for human language technology: introducing an LRE special section [<Journal>] Branco, António [Verfasser]; Cohen, Kevin Bretonnel [Sonstige]; Vossen, Piek [Sonstige]. DNB Subject Category Language Show details 17 Erratum to: Replicability and reproducibility of research results for human language technology: introducing an LRE special section [<Journal>] Branco, António [Verfasser]; Cohen, Kevin Bretonnel [Sonstige]; Vossen, Piek [Sonstige]. DNB Subject Category Language Show details 18 Modelling semantic relations with distributitional semantics and deep learning : question answering, entailment recognition and paraphrase detection Maraev, Vladislav. - 2017 Abstract: Nesta dissertação apresenta-se uma abordagem à tarefa de modelar relações semânticas entre dois textos com base em modelos de semântica distribucional e em aprendizagem profunda. O presente trabalho tira partido de várias disciplinas da ciência cognitiva, com especial relevo para a computação, a linguística e a inteligência artificial, e com fortes influência da neurociência e da psicologia cognitiva. Os modelos de semântica distribucional (também conhecidos como ”word embeddings”) são usados para representar o significado das palavras. As representações semânticas das palavras podem ainda ser combinadas para obter o significado de um excerto de um texto recorrendo ao uso da aprendizagem profunda, isto é, com o apoio das redes neurais de convolução. Esta abordagen é utilizada para replicar a experiência realizada por Bogdanova et al. (2015) na tarefa de deteção de perguntas que podem ser respondidas as mesmas respostas tal como estas foram respondidas em fóruns on-line. Os resultados do desempenho obtidos pelas experiências apresentadas nesta dissertação são equivalentes ou melhores que os resultados obtidos no trabalho de referência mencionado acima. Apresentao também um estudo sobre o impacto do pré-processamento apropriado do texto, tendo em conta os resultados que podem ser obtidos pelas abordagens adotadas no trabalho de referência supramencionado. Este estudo é levado a cabo removendo-se certas pistas que podem levar o sistema, indevidamente, a detetar perguntas equivalentes. Essa remoção das pistas leva a uma diminuição significativa no desempenho do sistema desenvolvido no trabalho de referência. Nesta dissertação é ainda apresentado um estudo sobre o impacto que os word embeddings treinados previamente têm na tarefa de detetar perguntas semanticamente equivalentes. Substituindo-se, aleatoriamente, word embeddings previamente treinados por outros melhora-se o desempenho do sistema. Além disso, o modelo foi utilizado na tarefa de reconhecimento de implicações para Português, onde mostrou uma taxa de acerto similar à da baseline. Este trabalho também reporta os resultados da aplicação da abordagem adotada numa competição para a deteção de paráfrases em Russo. A configuração final apresenta duas melhorias: usa character embeddings em vez de word embeddings e usa vários filtros de convolução. Esta configuração foi testado na execução padrão da Tarefa 2 da competição relevante, e mostrou resultados competitivos. ; This dissertation presents an approach to the task of modelling semantic relations between two texts, which is based on distributional semantic models and deep learning. The present work takes advantage of various disciplines of cognitive science, mainly computation, linguistics and artificial intelligence, with strong influences from neuroscience and cognitive psychology. Distributional semantic models (also known as word embeddings) are used to represent the meaning of words. Word semantic representations can be further combined towards obtaining the meaning of a larger chunk of a text using a deep learning approach, namely with the support of convolutional neural networks. These approaches are used to replicate the experiment carried out, by Bogdanova et al. (2015), for the task of detecting questions that can be answered by exactly the same answer in online user forums. Performance results obtained by my experiments are comparable or better than the ones reported in that referenced work. I present also a study on the impact of appropriate text preprocessing with respect to the results that can be obtained by the approaches adopted in that referenced work. Removing certain clues that can unduly help the system to detect equivalent questions leads to a significant decrease in system’s performance supported by that referenced work. I also present a study of the impact that pre-trained word embeddings have in the task of detecting the semantically equivalent questions. Replacing pre-trained word embeddings by randomly initialised ones improves the performance of the system. Additionally, the model was applied to the task of entailment recognition for Portuguese and showed an accuracy on a level with the baseline. This dissertation also reports on the results of an experimental study on the application of the adopted approach to the shared task of sentence paraphrase detection in Russian. The final set up contained two improvements: it uses several convolutional filters and it uses character embeddings instead of word embeddings. It was tested in Task 2 standard run of the relevant shared task and it showed competitive results. Keyword: Domínio/Área Científica::Ciências Sociais::Ciências da Educação; Domínio/Área Científica::Ciências Sociais::Psicologia; Linguística cognitiva; Processamento da linguagem natural; Semântica; Teses de mestrado - 2017 URL: http://hdl.handle.net/10451/30183 BASE Hide details 19 Computational Processing of the Portuguese Language : 12th International Conference, PROPOR 2016, Tomar, Portugal, July 13-15, 2016, Proceedings Marques-Silva, João [Herausgeber]; Ribeiro, Ricardo [Herausgeber]; Quaresma, Paulo [Herausgeber]. - Cham : Springer International Publishing, 2016 DNB Subject Category Language Show details 20 QTLeap WSD/NED corpus Agirre, Eneko; Branco, António; Popel, Martin. - : University of the Basque Country, UPV/EHU, 2015. : Faculty of Science, Univeristy of Lisbon, FCUL, 2015. : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2015. : Bulgarian Academy of Sciences, IICT-BAS, 2015 BASE Show details Page: 1 2 3 4

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