Named Entity Recognition With Parallel Recurrent Neural Networks
Andrej Žukov-Gregorič, Yoram Bachrach, Sam Coope · 2018
We present a new architecture for named entity recognition.Our model employs multiple independent bidirectional LSTM units across the same input and promotes diversity among them by employing an inter-model regularization term.By distributing computation across multiple smaller LSTMs we find a reduction in the total number of parameters.We find our architecture achieves state-of-the-art performance on the CoNLL 2003 NER dataset.