Papers › FLERT: Document-Level Features for Named Entity Recognition
FLERT: Document-Level Features for Named Entity Recognition
Stefan Schweter, Alan Akbik
Current state-of-the-art approaches for named entity recognition (NER) typically consider text at the sentence-level and thus do not model information that crosses sentence boundaries. However, the use of transformer-based models for NER offers natural options for capturing document-level features. In this paper, we perform a comparative evaluation of document-level features in the two standard NER architectures commonly considered in the literature, namely "fine-tuning" and "feature-based LSTM-CRF". We evaluate different hyperparameters for document-level features such as context window size and enforcing document-locality. We present experiments from which we derive recommendations for how to model document context and present new state-of-the-art scores on several CoNLL-03 benchmark datasets. Our approach is integrated into the Flair framework to facilitate reproduction of our experiments.
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Code
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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 2002 (Dutch) | FLERT XLM-R | F1 | 95.21 | #2 of 6 | Archive leaderboard | report |
| Named Entity Recognition (NER) | CoNLL 2002 (Spanish) | FLERT XLM-R | F1 | 90.14 | #4 of 6 | Archive leaderboard | report |
| Named Entity Recognition (NER) | CoNLL 2003 (English) | FLERT XLM-R | F1 | 94.09 | #6 of 73 | Archive leaderboard | report |
| Named Entity Recognition (NER) | CoNLL 2003 (German) | FLERT XLM-R | F1 | 88.34 | #2 of 6 | Archive leaderboard | report |
| Named Entity Recognition (NER) | CoNLL 2003 (German) Revised | FLERT XLM-R | F1 | 92.23 | #1 of 5 | Archive leaderboard | report |
| Named Entity Recognition (NER) | FindVehicle | FLERT | F1 Score | 80.9 | #2 of 3 | 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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