  This paper describes our approach presented for the eHealth-KD 2019
challenge. Our participation was aimed at testing how far we could go using
generic tools for Text-Processing but, at the same time, using common
optimization techniques in the field of Data Mining. The architecture proposed
for both tasks of the challenge is a standard stacked 2-layer bi-LSTM. The main
particularities of our approach are: (a) The use of a surrogate function of F1
as loss function to close the gap between the minimization function and the
evaluation metric, and (b) The generation of an ensemble of models for
generating predictions by majority vote. Our system ranked second with an F1
score of 62.18% in the main task by a narrow margin with the winner that scored
63.94%.

Computation and Language;Machine Learning