title: Classification techniques with minimal labelling effort and application to medical reports.
abstract: There are a number of approaches to classify text documents. Here, we use Partially Supervised Classification (PSC) and argue that it is an effective and efficient approach for real-world problems. PSC uses a two-step strategy to cut down on the labelling effort. There are a number of methods that have been proposed for each step. An evaluation of various methods is conducted using real-world medical documents. The results show that using EM to build the classifier yields better results than SVM. We also experimentally show that careful selection of a subset of features to represent the documents can improve performance.
authors.affiliation: School of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, UK. fathi.saad@uea.ac.uk
authors.forename: Fathi H
authors.initials: FH
authors.lastname: Saad
authors.forename: G Duncan
authors.initials: GD
authors.lastname: Bell
authors.forename: Beatriz
authors.initials: B
authors.lastname: de la Iglesia
mesh_terms: D001185:Artificial Intelligence; D003628:Database Management Systems; D004282:Documentation / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D006113:United Kingdom