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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... steps: initially a set of tagging rules is learned and then additional rules are induced to correct mistakes and imprecision in extraction. Three types of rules are defined in (LP)2: tagging rules, contextual rules, and correction rules ...
... steps: The tagging rules are induced as follows: First, a tag in the training corpus is selected, and a window of w words to ... step. • Correction rules are used to correct mistaken extractions. • All the identified boundaries are to be ...
... step, LearnDetector searches for the best extension of length L (a lookahead parameter) or less to the prefix and suffix of the current detector. The procedure returns when no extension yields a better score than the current detector ...
... steps. The best label sequence can be found using Viterbi algorithm. therefore, possible to find the sequence of POS tags that best accounts for any given sentence by identifying the sequence of states most likely to have been traversed ...
... step corresponds to the weight of the feature fk.t. λ k The most probable labeling sequence for an input x: Conditional Random Fields (CRFs) CRFs are undirected graphical model trained to maximize a conditional probability. CRFs can be ...
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 |