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

ACL 2016 Xuezhe Ma • Eduard Hovy

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.

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Evaluation


Task Dataset Model Metric name Metric value Global rank Compare
Named Entity Recognition CoNLL 2003 (English) Ma and Hovy F1 91.21 # 13
Part-Of-Speech Tagging Penn Treebank Ma and Hovy Accuracy 97.55 # 6