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Indexed by:会议论文

Date of Publication:2016-01-01

Included Journals:CPCI-S

Page Number:794-794

Key Words:disease; named entity recognition; convolutional neural network; deep learning; multiple label strategy

Abstract:In this paper, we present a deep learning based disease named entity recognition architecture. First, the word-level embedding, character-level embedding and lexicon feature embedding are concatenated as input. Then multiple convolutional layers are stacked over the input to extract useful features automatically. Finally, multiple label strategy, which is firstly introduced, is applied to the output layer to capture the correlation information between neighboring labels. Experimental results on both NCBI and CDR corpora show that ML-CNN can achieve the state-of-the-art performance.