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.
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