Named Entity Recognition with stack residual LSTM and trainable bias decoding

Understand

Recurrent Neural Network models are the state-of-the-art for Named Entity Recognition (NER).

• We present two innovations to improve the performance of these models.
• The first innovation is the introduction of residual connections between the Stacked Recurrent Neural Network model to address the degradation problem of deep neural networks.
• The second innovation is a bias decoding mechanism that allows the trained system to adapt to non-differentiable and externally computed objectives, such as the entity-based F-measure.