Emerging Technologies of Text Mining: Techniques and Applications: Techniques and Applicationsdo Prado, Hercules Antonio, Ferneda, Edilson IGI Global, 2007 M10 31 - 376 páginas Massive amounts of textual data make up most organizations' stored information. Therefore, there is increasingly high demand for a comprehensive resource providing practical hands-on knowledge for real-world applications. Emerging Technologies of Text Mining: Techniques and Applications provides the most recent technical information related to the computational models of the text mining process, discussing techniques within the realms of classification, association analysis, information extraction, and clustering. Offering an innovative approach to the utilization of textual information mining to maximize competitive advantage, Emerging Technologies of Text Mining: Techniques and Applications will provide libraries with the defining reference on this topic. |
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... documents by using semantic information generated at the document sentence level. This information is obtained from a document profile model built by applying a frequent word sequence method. The agglomerative hierarchical method is ...
... document searching, Web text processing, text feature conversion, and the neural model building. The scalability of ... documents, users prefer to reformulate their query instead of sifting through the numerous pages of search results ...
... documents clustering techniques, an alternative to cluster textual documents using concepts is presented in this chapter. The use of concepts instead of simple words adds semantics to the document clustering process, leading to a better ...
... document or a set of documents. Information extraction techniques are able to find relevant data or expressions inside documents. Clustering is applied to discover underlying structures in a set of documents. This book aims to provide ...
... documents clustering techniques, Chapter XI introduces an alternative to cluster textual documents using concepts. The use of concepts instead of simple words adds semantics to the document clustering process, leading to a better ...
Contenido
1 | |
Creating Strategic Information for Organizations with Structured Text | 34 |
Automatic NLP for Competitive Intelligence | 54 |
Mining Profiles and Definitions with Natural Language Processing | 77 |
Deriving Taxonomy from Documents at Sentence Level | 99 |
Rule Discovery from Textual Data | 120 |
Exploring Unclassified Texts Using Multiview Semisupervised Learning | 139 |
A MultiAgent Neural Network System for Web Text Mining | 162 |
AntWebWeb Search Based on Ant Behavior Approach and Implementation in Case of Interlegis | 208 |
Conceptual Clustering of Textual Documents and Some Insights for Knowledge Discovery | 223 |
A Hierarchical Online Classifier for Patent Categorization | 244 |
Text Mining to Define a Validated Model of Hospital Rankings | 268 |
An Interpretation Process for Clustering Analysis Based on the Ontology of Language | 297 |
Compilation of References | 321 |
About the Contributors | 348 |
Index | 355 |
Otras ediciones - Ver todas
Emerging Technologies of Text Mining: Techniques and Applications Hercules Antonio do Prado,Edilson Ferneda Sin vista previa disponible - 2008 |