Papers › End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
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. 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 the two data --- 97.55\% accuracy for POS tagging and 91.21\% F1 for NER.
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Code
Syntology Ran 4 of 24 code samples harvested from 5 repositories linked to this paper; 20 have no recorded run. Of those that ran: 2 ran · honoured contract; 2 ran · our draft was wrong.
By repository: community (archive-listed): 23 samples from 5 repositories, 3 ran; 1 identical to code first harvested elsewhere. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
25 repositories listed; official and paper-mentioned ones first.
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Code Syntology ran Syntology
24 samples harvested; 4 ran; 2 honoured the contract we drafted; 20 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Named Entity Recognition (NER) | CoNLL 2003 (English) | BLSTM-CNN-CRF | F1 | 91.21 | #68 of 73 | Archive leaderboard | report |
| Named Entity Recognition (NER) | CoNLL++ | BiLSTM-CNN-CRF | F1 | 91.87 | #10 of 11 | Archive leaderboard | report |
| Part-Of-Speech Tagging | Penn Treebank | BLSTM-CNN-CRF | Accuracy | 97.55 | #11 of 20 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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