  We compare the use of LSTM-based and CNN-based character-level word
embeddings in BiLSTM-CRF models to approach chemical and disease named entity
recognition (NER) tasks. Empirical results over the BioCreative V CDR corpus
show that the use of either type of character-level word embeddings in
conjunction with the BiLSTM-CRF models leads to comparable state-of-the-art
performance. However, the models using CNN-based character-level word
embeddings have a computational performance advantage, increasing training time
over word-based models by 25% while the LSTM-based character-level word
embeddings more than double the required training time.
