  Previous studies have shown that health reports in social media, such as
DailyStrength and Twitter, have potential for monitoring health conditions
(e.g. adverse drug reactions, infectious diseases) in particular communities.
However, in order for a machine to understand and make inferences on these
health conditions, the ability to recognise when laymen's terms refer to a
particular medical concept (i.e.\ text normalisation) is required. To achieve
this, we propose to adapt an existing phrase-based machine translation (MT)
technique and a vector representation of words to map between a social media
phrase and a medical concept. We evaluate our proposed approach using a
collection of phrases from tweets related to adverse drug reactions. Our
experimental results show that the combination of a phrase-based MT technique
and the similarity between word vector representations outperforms the
baselines that apply only either of them by up to 55%.
