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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...
In the era of big data, Content-Based Image Retrieval combined with deep learning technology gradually becomes the mainstream. This method can overcome some drawbacks of traditional CBIR, but at the same time there are still some problems to be solved, such as: The extracted feature dimension (generally more than 2000) is higher, which is not beneficial for efficient data storage and fast real-time...
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