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.
Compared with traditional approaches, neural networks based approaches do not require hand-crafted features and achieved state-of-the-art performance on name tagging REF .
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
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Computer Science Mathematics