End to end Sequence Labeling via Bi directional LSTM CNNs CRF
State of the art sequence labeling systems traditionally require large amounts of task specific knowledge in the form of hand crafted features and data pre processing. In this paper, we introduce a novel neutral network architecture that benefits from both word and character level representations automatically, by using combination of bidirectional LSTM, CNN and CRF. Our system is truly end to end, requiring no feature engineering or data pre processing, thus making it applicable to a wide range of sequence labeling tasks. We evaluate our system on two data sets for two sequence labeling tasks Penn Treebank WSJ corpus for part of speech (POS) tagging and CoNLL 2003 corpus for named entity recognition (NER) We obtain state of the art performance on both the two data 97.55\ accuracy for POS tagging and 91.21\ F1 for NER.
ACL