Deep neural model with enhanced embeddings for pharmaceutical and chemical entities recognition in Spanish clinical text
Renzo Rivera, Paloma Martı́nez · 2019
In this work, we introduce a Deep Learning architecture for pharmaceutical and chemical Named Entity Recognition in Spanish clinical cases texts.We propose a hybrid model approach based on two Bidirectional Long Short-Term Memory (Bi-LSTM) network and Conditional Random Field (CRF) network using character, word, concept and sense embeddings to deal with the extraction of semantic, syntactic and morphological features.The approach was evaluated on the Pharma-CoNER Corpus obtaining an F-measure of 85.24% for subtask 1 and 49.36% for sub-task2.These results prove that deep learning methods with specific domain embedding representations can outperform the state-of-theart approaches.