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Supporting Access to Textual Resources Using Named Entity Recognition and Document Classification
This dissertation addresses the challenges of accessing textual resources and the resulting Information Overload faced by software developers on the Web. A new approach based on methods of Named Entity Recognition and rule-based Document Classification is proposed to facilitate the extraction of domain-related named entities, enabling faceted search and browsing in an innovative content and knowledge management ecosystem portal. The concept of the approach with model designs, overall architecture, and the initial prototype is described. Finally, several evaluations are shown to demonstrate the feasibility of the approach and usability of the prototype.
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