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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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Abstract:
The work presented here addresses the use of unmarked contexts in pattern-based nominal lexical semantic classification. We define unmarked contexts to be the counterposition of the class-indicatory, or marked, contexts. Its aim is to evaluate how unmarked contexts can be used to improve the accuracy and reliability of lexical semantic classifiers. Results demonstrate that the combined use of both types of distributional in-formation (marked and unmarked) is crucial to improve classification. This result was replicated using two different corpora, demonstrating the robustness of the method proposed. ; info:eu-repo/semantics/publishedVersion
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URL: http://hdl.handle.net/10451/33006
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LexTec - a rich language resource for technical domains in Portuguese
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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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