Um sistema de disseminação seletiva da informação baseado em Cross-Document Structure Theory

• Autor(a) principal: Beltrame, Walber Antonio Ramos
• Data de Publicação: 2011
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da Universidade Federal do Espírito Santo (riUfes)
• Texto Completo: http://repositorio.ufes.br/handle/10/6414
• topic: Ciência da Computação; Disseminação seletiva da informação; Recuperação da informação; Processamento de linguagem natural (Computação); Teoria dos grafos; Sistemas de recuperação da informação; 004

A System for Selective Dissemination of Information is a type of information system that aims to harness new intellectual products, from any source, for environments where the probability of interest is high. The inherent challenge is to establish a computational model that maps specific information needs, to a large audience, in a personalized way. Therefore, it is necessary to mediate informational structure of unit, so that includes a plurality of attributes to be considered by process of content selection. In recent publications, systems are proposed based on text markup data (meta-data models), so that treatment of manifest information between computing semi-structured data and inference mechanisms on meta-models. Such approaches only use the data structure associated with the profile of interest. To improve this characteristic, this paper proposes construction of a system for selective dissemination of information based on analysis of multiple discourses through automatic generation of conceptual graphs from texts, introduced in solution also unstructured data (text). The proposed model is motivated by Cross-Document Structure Theory, introduced in area of Natural Language Processing, focusing on automatic generation of summaries. The model aims to establish correlations between semantic of discourse, for example, if there are identical information, additional or contradictory between multiple texts. Thus, an aspects discussed in this dissertation is that these correlations can be used in process of content selection, which had already been shown in other related work. Additionally, the algorithm of the original model is revised in order to make it easy to apply.