State-of-the-art sequence labeling systems traditionally require large amounts of taskspecific knowledge in the form of handcrafted 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 preprocessing, 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 datasets -97.55% accuracy for POS tagging and 91.21% F1 for NER.
REF use CNNs rather than LSTMs to compose characters in a Bi-LSTM-CRF, achieving state-ofthe-art performance on part-of-speech tagging and CoNLL NER without lexicons.
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
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Computer Science Mathematics