Biomedical named entity recognition based on the two channels and sentence-level reading control conditioned LSTM-CRF

Biomedical named entity recognition (Bio-NER) is an important preliminary step for many biomedical text mining tasks. The current mainstream methods for NER are based on the neural networks to avoid the complex hand-designed features derived from various linguistic analyses. However, the performance of these methods is always limited to exploring dependencies across output label and ignoring some potential word-level and sentence-level semantic information. Therefore, we propose a novel Long Short Term Memory (LSTM) Networks model SC-LSTM-CRF integrating two channels and the sentence-level embeddings for Bio-NER. In our model, two channels word embeddings are introduced as the input to obtain more abundant potential information, and the sentence-level reading control gate (SC) is inserted into the networks to integrate the implicit meaning of an entire sentence. The experimental results conducted on the BioCreative II GM corpus show that our method can achieve an F-score of 89.49%, which outperforms all state-of-the-art systems and is 0.88% higher than the best performing neural networks.