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Table 1 The proposed network architecture

From: A multitask bi-directional RNN model for named entity recognition on Chinese electronic medical records

Name Description
Input Sentences in EMR
Word Embedding Mikolov model
Character Embedding Layer 150 LSTM cells for each hidden layer,
  one forward hidden layer and one backward hidden layer,
  Dropout = 0.5
Parts-of-speech tag (POS) layer 150 LSTM cells for each hidden layer,
  one forward hidden layer and one backward hidden layer,
  Dropout = 0.5
Named Entity recognition (NER) Layer 150 LSTM cells for each hidden layer,
  one forward hidden layer and one backward hidden layer,
  Dropout = 0.5
Output Softmax