Lexical acquisition for clinical text mining using distributional similarity

We describe experiments into the use of distributional similarity for acquiring lexical information from clinical free text, in particular notes typed by primary care physicians (general practitioners). We also present a novel approach to lexical acquisition from ‘sensitive' text, which does not require the text to be manually anonymised --- a very expensive process --- and therefore allows much larger datasets to be used than would normally be possible.