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Using Text Mining for Unsupervised Knowledge Extraction and Organization

Author(s): REZENDE, S. O. | MARCACINI, R. M. | MOURA, M. F.

Journal: Salesian Journal on Information Systems
ISSN 1983-5604

Issue: 7;
Start page: 7;
Date: 2011;
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Keywords: Text Mining | Document Clustering | Unsupervised Learning | Metadata Extraction | Topic hierarchy

The progress in digitally generated data aquisition and storage has allowed for a huge growth in information generated in organizations. Around 80% ofthose data are created in non structured format and a significant part of those are texts. Intelligent organization of those textual collection is a matter of interest for most organizations, for it speed up information search and retrieval. In this context, Text Mining can transform this great amount non structure text data un useful knowledge, that can even be innovative for those organizations. Using unsupervised methods for knowledge extraction and organization has received great attention in literature, because it does not require previous knowledge on the textual collections that are going to be explored. In this article we describe the main techniques and algorithms used for unsupervised knowledege extraction and organization from textual data. The most relevant works in literature are presented and discussed in each phase of the Text Mining process and some existing computational tools are suggested for each task at hand. At last, some examples and applications are present to show the use of Text Mining on real problems.
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