title: Clinical text classification under the Open and Closed Topic Assumptions.
abstract: This paper investigates multi-topic aspects in automatic classification of clinical free text in comparison with general text. In this paper, we facilitate two different views on multi-topics: the Closed Topic Assumption (CTA) and the Open Topic Assumption (OTA). Experimental results show that the characteristics of multi-topic assignments in the Computational Medicine Centre (CMC) Medical NLP Challenge Data is strongly OTA-oriented but general text Reuters-21578 is characterised in the middle of the OTA and CTA spectrum.
authors.affiliation: School of Computer Science, University of Manchester MIB, 131 Princess Street, Manchester, M1 7DN, UK. Yutaka.Sasaki@manchester.ac.uk
authors.forename: Yutaka
authors.initials: Y
authors.lastname: Sasaki
authors.forename: Brian
authors.initials: B
authors.lastname: Rea
authors.forename: Sophia
authors.initials: S
authors.lastname: Ananiadou
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016347:Medical Records Systems, Computerized / Q000145:classification*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated