Elementos de interoperabilidade na perspectiva da catalogação descritiva

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Recuperação de informação de música e dados ID3: possíveis aplicações

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aboutness em Análise Documental de textos literários infanto-juvenis: perspectivas para o aprimoramento da representação de conteúdo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Avaliações externas e em larga escala nas redes de educação básica dos Estados brasileiros

Pós-graduação em Educação - FCT

Relação entre as habilidades metalinguísticas, leitura e compreensão leitora em escolares com Transtorno do Déficit de Atenção com Hiperatividade

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A exaustividade e a especificidade como valores éticos no processo de indexação: aspectos conceituais e deontológicos

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Ontologias no processo de indexação automática de documentos textuais

Pós-graduação em Ciência da Informação - FFC

Comportamento informacional cotidiano de adolescentes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

SNIIC: a plataforma digital de cultura brasileira sob a perspectiva das convergências entre o Design e a Ciência da Informação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A exaustividade e a especificidade como valores éticos no processo de indexação: aspectos conceituais e deontológicos

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Ontologias no processo de indexação automática de documentos textuais

Pós-graduação em Ciência da Informação - FFC

Comportamento informacional cotidiano de adolescentes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

SNIIC: a plataforma digital de cultura brasileira sob a perspectiva das convergências entre o Design e a Ciência da Informação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Class-specific metrics for multidimensional data projection applied to CBIR

Content-based image retrieval is still a challenging issue due to the inherent complexity of images and choice of the most discriminant descriptors. Recent developments in the field have introduced multidimensional projections to burst accuracy in the retrieval process, but many issues such as introduction of pattern recognition tasks and deeper user intervention to assist the process of choosing the most discriminant features still remain unaddressed. In this paper, we present a novel framework to CBIR that combines pattern recognition tasks, class-specific metrics, and multidimensional projection to devise an effective and interactive image retrieval system. User interaction plays an essential role in the computation of the final multidimensional projection from which image retrieval will be attained. Results have shown that the proposed approach outperforms existing methods, turning out to be a very attractive alternative for managing image data sets.

Structure-semantics interplay in complex networks and its effects on the predictability of similarity in texts

The classification of texts has become a major endeavor with so much electronic material available, for it is an essential task in several applications, including search engines and information retrieval. There are different ways to define similarity for grouping similar texts into clusters, as the concept of similarity may depend on the purpose of the task. For instance, in topic extraction similar texts mean those within the same semantic field, whereas in author recognition stylistic features should be considered. In this study, we introduce ways to classify texts employing concepts of complex networks, which may be able to capture syntactic, semantic and even pragmatic features. The interplay between various metrics of the complex networks is analyzed with three applications, namely identification of machine translation (MT) systems, evaluation of quality of machine translated texts and authorship recognition. We shall show that topological features of the networks representing texts can enhance the ability to identify MT systems in particular cases. For evaluating the quality of MT texts, on the other hand, high correlation was obtained with methods capable of capturing the semantics. This was expected because the golden standards used are themselves based on word co-occurrence. Notwithstanding, the Katz similarity, which involves semantic and structure in the comparison of texts, achieved the highest correlation with the NIST measurement, indicating that in some cases the combination of both approaches can improve the ability to quantify quality in MT. In authorship recognition, again the topological features were relevant in some contexts, though for the books and authors analyzed good results were obtained with semantic features as well. Because hybrid approaches encompassing semantic and topological features have not been extensively used, we believe that the methodology proposed here may be useful to enhance text classification considerably, as it combines well-established strategies. (c) 2012 Elsevier B.V. All rights reserved.