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 feat...Show MoreMetadata
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.
Date of Conference: 15-18 December 2016
Date Added to IEEE Xplore: 19 January 2017
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