Adapting Phrase-based Machine Translation to Normalise Medical Terms in Social Media Messages
This work addresses the need for accurate text normalization in social media health monitoring, enabling better machine understanding of health conditions, though it is incremental as it adapts existing methods.
The paper tackled the problem of normalizing laymen's medical terms in social media to standard medical concepts, using an adapted phrase-based machine translation technique combined with word vectors, achieving up to 55% improvement over baselines in experiments on tweets related to adverse drug reactions.
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%.