Many applications in text processing require significant human effort for either labeling large document collections (when learning statistical models) or extrapolating rules from...
This paper studies noise reduction for computational efficiency improvements in a statistical learning method for text categorization, the Linear Least Squares Fit (LLSF) mapping...
Ordering principles of digital libraries expressed in ontologies may be highly heterogeneous even within a domain and especially over different cultures. Automatic methods for mapp...
Abstract. We focus on two recently proposed algorithms in the family of “boosting”-based learners for automated text classification, AdaBoost.MH and AdaBoost.MHKR . While the ...
Pio Nardiello, Fabrizio Sebastiani, Alessandro Spe...
This paper presents a cluster-based text categorization system which uses class distributional clustering of words. We propose a new clustering model which considers the global in...