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. |
Dentro del libro
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... specific trails with stronger concentration of pheromone can guide the ants in finding shorter paths between their nest and the food source. AntWeb, the system resulting from the application of this metaphor, is described along with ...
... specific trails with stronger concentration of pheromone can guide the ants in finding shorter paths between their nest and the food source. AntWeb, the system resulting from the application of this metaphor, is described along with ...
... specific information on the basis of the predefined metadata. In training, the model(s) are constructed to detect the subsequence. In the models, the input data is viewed as a sequence of instances. For example, a document can be viewed ...
... specific type of information. For IE from text, the basic unit that we are dealing with can be tokens or text-lines in the text. (Hereafter, we will use token as the basic unit in our explanation.) We then try to learn two classifiers ...
... specific classification method that is able to learn a better classifier on the unbalanced data. We have investigated the unbalanced classification model of SVMs (Support Vector Machines). Using the same notations in section ...
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 |