Fig. 5 

The architecture of BiLSTM-CRF model for PNER. In embedding layer, “w2v” means the word embeddings pre-trained using the word2vec tool, character embedding can be learned by Character-level BiLSTM, “biLM” means the pre-trained bi-directional Language Model ELMo, “Randomly initialized” means obtaining a corresponding vector in a random manner. Six feature representations of each word are concatenated together to form an input and fed to a BiLSTM layer. The last is the CRF layer, which is used to decode the best tag path in all possible tag paths. The input sentence is “SyGCaMP5 and MtDsRed or myc - ΔEF - Miro1 - IRES - MtDsRed ( ΔEF Miro) with and without TTX treatments”. The output tag sequence is “OOOOBOOOBOOOOOOOOOOOBO”