Clinical text classification under the Open and Closed Topic Assumptions
by Yutaka Sasaki, Brian Rea, Sophia Ananiadou
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 3, No. 3, 2009

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.

Online publication date: Tue, 23-Jun-2009

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