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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 Hits 1 – 17 of 17 1 Análise sintática automática de texto real com estruturas desviantes: o desempenho de sistemas de parsing baseados em dependências com textos de aprendentes de Português L2/LE Vaz, Mafalda Silva. - 2022 BASE Show details 2 Reflexão sobre opções linguísticas em Tradução: traduções jurídicas oficiais inglês-português e português-inglês Guo, Jiayuan. - 2022 BASE Show details 3 Relatório de estágio no Camões, I.P. : análise de fenómenos lexicais na tradução de textos técnicos Bradatian, Eliza. - 2021 BASE Show details 4 Relatório de estágio curricular no Camões, I.P. : léxico e tradução Costa, Luís Filipe de Oliveira. - 2020 BASE Show details 5 Quality in human post-editing of machine-translated texts : error annotation and linguistic specifications for tackling register errors Testa, Ingrid. - 2019 BASE Show details 6 Using unmarked contexts in nominal lexical semantic classification Romeo, Lauren; Mendes, Sara; Bel, Núria. - : Dublin City University and Association for Computational Linguistics, 2018 BASE Show details 7 LexTec - a rich language resource for technical domains in Portuguese Marrafa, Palmira; Amaro, Raquel; Mendes, Sara. - : European Language Resources Association, 2018 BASE Show details 8 A cascade approach to complex-type classification Bel, Núria; Romeo, Lauren; Mendes, Sara. - : European Language Resources Association, 2018 BASE Show details 9 Relatório de estágio no Camões, I.P. : discussão sobre opções linguísticas relevantes para a tradução Jesus, Nuno João Gonçalves de. - 2017 BASE Show details 10 Quality in machine translation and human post-editing : error annotation and specifications Comparin, Lucia. - 2017 BASE Show details 11 Using qualia information to identify lexical semantic classes in an unsupervised clustering task ... Romeo, Lauren; Mendes, Sara; Bel, Núria. - : arXiv, 2013 BASE Show details 12 Syntax and semantics of adjectives in portuguese analysis and modeling Mendes, Sara, 1978-. - 2010 BASE Show details 13 Towards the automatic classification of complex-type nominals Romeo, Lauren; Mendes, Sara; Bel Rafecas, Núria. - : ACL (Association for Computational Linguistics) BASE Show details 14 A cascade approach for complex-type classification Romeo, Lauren; Mendes, Sara; Bel Rafecas, Núria. - : ACL (Association for Computational Linguistics) BASE Show details 15 Using qualia information to identify lexical semantic classes in an unsupervised clustering task Bel Rafecas, Núria; Romeo, Lauren; Mendes, Sara. - : ACL (Association for Computational Linguistics) BASE Show details 16 Using unmarked contexts in nominal lexical semantic classification Romeo, Lauren; Mendes, Sara; Bel Rafecas, Núria. - : Association for Computational Linguistics (ACL) BASE Show details 17 Reading between the lines: overcoming data sparsity for accurate classification of lexical relationships Navigli, Roberto; Necşulescu, Silvia; Mendes, Sara; Bel Rafecas, Núria; Jurgens, David. - : ACL (Association for Computational Linguistics) Abstract: Comunicació presentada a: 4th Joint Conference on Lexical and Computational Semantics, celebrada a Denver, Colorado, Estats Units d'Amèrica, del 4 al 5 de juny de 2015. ; The lexical semantic relationships between word pairs are key features for many NLP tasks. Most approaches for automatically classifying related word pairs are hindered by data sparsity because of their need to observe two words co-occurring in order to detect the lexical relation holding between them. Even when mining very large corpora, not every related word pair co-occurs. Using novel representations based on graphs and word embeddings, we present two systems that are able to predict relations between words, even when these are never found in the same sentence in a given corpus. In two experiments, we demonstrate superior performance of both approaches over the state of the art, achieving significant gains in recall. ; The authors gratefully acknowledge the support of the CLARA project (EU-7FP-ITN-238405), the SKATER project (Ministerio de Economia y Competitividad, TIN2012-38584-C06-05) and of the MultiJEDI ERC Starting Grant no. 259234. URL: http://hdl.handle.net/10230/36672 BASE Hide details

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