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.
|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|