Papers › Named Entity Recognition With Parallel Recurrent Neural Networks
Named Entity Recognition With Parallel Recurrent Neural Networks
Andrej {\v{Z}}ukov-Gregori{\v{c}}, Yoram Bachrach, Sam Coope
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 significant reduction in the total number of parameters. We find our architecture achieves state-of-the-art performance on the CoNLL 2003 NER dataset.
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