Full Description
Text Mining - Theoretical Aspects and Applications presents contributions from researchers from different disciplines. Each of them is studying the problem of mining text according to his scientific background: artificial intelligence, computational linguistics, document analysis, machine learning, information retrieval, pattern recognition. Their common goal is to analyse huge text collections in real world applications in order to support knowledge-intensive processes.
Contents
I. Renz, J. Franke: Text Mining.- N. Fuhr: XML Information Retrieval and Information Extraction.- J. Zavrel, W. Daelemans: Feature-Rich Memory-Based Classification for Shallow NLP and Information Extraction.- R. Klinkenberg, S. Ruping: Concept Drift and the Importance of Examples.- T. Joachims: Evaluating Retrieval Performance Using Clickthrough Data.- A. Hust et al.: Towards Collaborative Information Retrieval: Three Approaches.- D. Rosner, M. Kunze: The XDOC Document Suite - a Workbench for Document Mining.- A. Hotho et al.: On Knowledgeable Unsupervised Text Mining.- F. Ceravegna et al.: Using Adaptive Information Extraction for Effective Human-Centred Document Annotation.
