Clinical text classification under the Open and Closed Topic Assumptions.
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.
Sasaki
Yutaka
Y
School of Computer Science, University of Manchester MIB, 131 Princess Street, Manchester, M1 7DN, UK. Yutaka.Sasaki@manchester.ac.uk
Rea
Brian
B
Ananiadou
Sophia
S
D000465:Algorithms; D001185:Artificial Intelligence; D016347:Medical Records Systems, Computerized / Q000145:classification*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated