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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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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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Abstract:
Acquiring lexical information is a complex problem, typically approached by relying on a number of contexts to contribute information for classification. One of the first issues to address in this domain is the determination of such contexts. The work presented here proposes the use of automatically obtained FORMAL role descriptors as features used to draw nouns from the same lexical semantic class together in an unsupervised clustering task. We have dealt with three lexical semantic classes (HUMAN, LOCATION and EVENT) in English. The results obtained show that it is possible to discriminate between elements from different lexical semantic classes using only FORMAL role information, hence validating our initial hypothesis. Also, iterating our method accurately accounts for fine-grained distinctions within lexical classes, namely distinctions involving ambiguous expressions. Moreover, a filtering and bootstrapping strategy employed in extracting FORMAL role descriptors proved to minimize effects of sparse ... : 10 pages, 5 tables. Also available in UPF institutional repository (http://hdl.handle.net/10230/20383) ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.1303.2449 https://arxiv.org/abs/1303.2449
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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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