Bidirectional LSTM-CRF Models for Sequence Tagging
Table 2: Comparison of tagging performance on POS, chunking and NER tasks for various models.
['[EMPTY]', 'Conv-CRF ', 'POS 96.37', 'CoNLL2000 90.33', 'CoNLL2003 81.47']
We use a learning rate of 0.1 to train models. We set hidden layer size to 300 and found that model performance is not sensitive to hidden layer sizes. The training for three tasks require less than 10 epochs to converge and it in general takes less than a few hours. The POS task is evaluated by computing per-word accuracy, while the chunk and NER tasks are evaluated by computing F1 scores over chunks.