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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
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Reflexão sobre opções linguísticas em Tradução: traduções jurídicas oficiais inglês-português e português-inglês
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Relatório de estágio no Camões, I.P. : análise de fenómenos lexicais na tradução de textos técnicos
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Relatório de estágio curricular no Camões, I.P. : léxico e tradução
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Quality in human post-editing of machine-translated texts : error annotation and linguistic specifications for tackling register errors
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Using unmarked contexts in nominal lexical semantic classification
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LexTec - a rich language resource for technical domains in Portuguese
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Abstract:
The growing amount of available information and the importance given to the access to technical information enhance the potential role of NLP applications in enabling users to deal with information for a variety of knowledge domains. In this process, language resources are crucial. This paper presents Lextec, a rich computational language resource for technical vocabulary in Portuguese. Encoding a representative set of terms for ten different technical domains, this concept-based relational language resource combines a wide range of linguistic information by integrating each entry in a domain-specific wordnet and associating it with a precise definition for each lexicalization in the technical domain at stake, illustrative texts and information for translation into English. ; info:eu-repo/semantics/publishedVersion
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Keyword:
Concept-based language resources; Relational models of the lexicon; Technical lexica
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URL: http://hdl.handle.net/10451/33008
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Relatório de estágio no Camões, I.P. : discussão sobre opções linguísticas relevantes para a tradução
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Quality in machine translation and human post-editing : error annotation and specifications
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Using qualia information to identify lexical semantic classes in an unsupervised clustering task ...
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Syntax and semantics of adjectives in portuguese analysis and modeling
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Towards the automatic classification of complex-type nominals
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Using qualia information to identify lexical semantic classes in an unsupervised clustering task
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Using unmarked contexts in nominal lexical semantic classification
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Reading between the lines: overcoming data sparsity for accurate classification of lexical relationships
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