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Adverse drug reactions (ADRs) are unwanted or harmful effects experiencedafter the administration of a certain drug or a combination of drugs,presenting a challenge for drug development and drug administration. In thispaper, we present a set of taggers for extracting adverse drug reactions andrelated entities, including factors, severity, negations, drug class andanimal. The systems used a mix of rule-based, machine learning (CRF) and deeplearning (BLSTM with word2vec embeddings) methodologies in order to annotatethe data. The systems were submitted to adverse drug reaction shared task,organised during Text Analytics Conference in 2017 by National Institute forStandards and Technology, archiving F1-scores of 76.00 and 75.61 respectively.

Extracting adverse drug reactions and their context using sequence labelling ensembles in TAC2017