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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... Figure 1. Example of AutoSlog concept node Concept Node: Name: target-subject-passive-verb-bombed Trigger: bombed Variable Slots: (target (*S *1)) Constraints: (class phys-target *S*) Constant Slots: (type bombing) Enabling Conditions ...
... Figure 2 has the following meaning: ignore all characters until you find the first occurrence of <B> and extract the ... Figure 2, we can see that the WIEN rule can be successfully to be applied to both documents D1 and D2. Figure 2 ...
... Figure 3. In Figure 3, E represents the example set; notation delimiter dates cands are generated l k given l (k, E) represent the candidates the example set E. for The candiby enumerating the suffixes of the shortest string occurring ...
... Figure 4 shows the learning algorithm in BWI. In BWI, AdaBoost algorithm runs in iterations. In each iteration, it outputs a weak learner (called hypotheses) from the training data and also a weight for the learner representing the ...
... Figure 5), then we start the search progress from the first start and view the tokens between the first token and the end token (i.e., “Professor Steve Skiena”) as the target. However, in some applications, the simple combination method ...
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 |