Lasige-BioTM at ProfNER: BiLSTM-CRF and contextual Spanish embeddings for Named Entity Recognition and Tweet Binary Classification

Abstract

The paper describes the participation of the Lasige-BioTM team at sub-tracks A and B of ProfNER, which was based on: i) a BiLSTM-CRF model that leverages contextual and classical word embeddings to recognize and classify the mentions, and ii) on a rule-based module to classify tweets. In the Evaluation phase, our model achieved a F1-score of 0.917 (0, 031 more than the median) in sub-track A and a F1-score of 0.727 (0, 034 less than the median) in sub-track B.