End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

TL;DR: This paper used a combination of bidirectional LSTM, CNN and CRF for sequence labeling tasks, and achieved state-of-the-art performance on both datasets for POS tagging and CoNLL 2003 corpus for NER.

Abstract: 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.