Serial and Parallel Recurrent Convolutional Neural Networks for Biomedical Named Entity Recognition.

Identifying named entities from unstructured biomedical text is an important part of information extraction. The irrelevant words in long biomedical sentences and the complex composition of the entity make LSTM used in the general domain less effective. We find that emphasizing the local connection between words in a biomedical entity can improve performance. Based on the above observation, this paper proposes two novel neural network architectures combining bidirectional LSTM and CNN. In the first architecture S-CLSTM, a CNN structure is built on the top of bidirectional LSTM to keep both long dependencies in a sentence and local connection between words. The second architecture P-CLSTM combines bidirectional LSTM and CNN in parallel with the weighted loss to take advantage of the complementary features of two networks. Experimental results indicate that our architectures achieve significant improvements compared with baselines and other state-of-the-art approaches.