A Framework for the Classification of Unstructured Data

Increased sources and quantity of unstructured information has created a further need for categorization and interpretation of their content. This paper describes the design of an interchangeable framework to support learning from an unstructured data source. Our approach supports integration of two or more learning mechanisms with a traditional indexing method. The goal is to identify a higher semantic content and more meaningful keyword combinations, considering both supervised and unsupervised techniques. Within a specific implementation both Bayesian learning as well as clustering are integrated to support a boost parameter towards classification of unstructured text. We find that an implementation of this framework applied towards a set of Reuters news feeds supports a vastly improved recognition rate. Our effort is directed towards making associations between structured and unstructured information.