  Named entity recognition (NER) is used to identify relevant entities in text.
A bidirectional LSTM (long short term memory) encoder with a neural conditional
random fields (CRF) decoder (biLSTM-CRF) is the state of the art methodology.
In this work, we have done an analysis of several methods that intend to
optimize the performance of networks based on this architecture, which in some
cases encourage overfitting avoidance. These methods target exploration of
parameter space, regularization of LSTMs and penalization of confident output
distributions. Results show that the optimization methods improve the
performance of the biLSTM-CRF NER baseline system, setting a new state of the
art performance for the CoNLL-2003 Spanish set with an F1 of 87.18.
