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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... developed many solutions by extending techniques from artificial intelligence, statistic, data bases, and information retrieval aiming to scale them to the new problems. Also, new techniques to extract knowledge from corporate databases ...
... developed in the fields of organizational and competitive intelligence, environmental scanning, prospective scenarios analysis, and business intelligence, among others, allowing the organizations to have a much more competitive position ...
... developed based on the method, including: AutoSlog (Riloff, 1993), Crystal (Soderland, Fisher, Aseltine, & Lehnert, 1995), (LP)2 (Ciravegna, 2001), iASA (Tang, Li, & Lu, 2005), Whisk (Soderland, 1999), Rapier (Califf & Mooney, 1998) ...
... developed to overcome the problem of word sparseness. For example, an author line “Chungki Lee James E. Burns” is represented as “CapNonDictWord: :MayName: :MayName: : SingleCap: :MayName:”, after word clustering. The weight ofa word ...
... developed a system based on the extracted person information, which is called academic research social networking mining system (Arnetminer, available at http://www. arnetminer.org). In Arnetminer, the user inputs a person name, and the ...
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 |
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Emerging Technologies of Text Mining: Techniques and Applications Hercules Antonio do Prado,Edilson Ferneda Sin vista previa disponible - 2008 |