Papers › Towards Improving Neural Named Entity Recognition with Gazetteers
Towards Improving Neural Named Entity Recognition with Gazetteers
Tianyu Liu, Jin-Ge Yao, Chin-Yew Lin
Most of the recently proposed neural models for named entity recognition have been purely data-driven, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features. This could increase the chance of overfitting since the models cannot access any supervision signal beyond the small amount of annotated data, limiting their power to generalize beyond the annotated entities. In this work, we show that properly utilizing external gazetteers could benefit segmental neural NER models. We add a simple module on the recently proposed hybrid semi-Markov CRF architecture and observe some promising results.
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 2003 (English) | HSCRF + softdict | F1 | 92.75 | #37 of 73 | Archive leaderboard | report |
| Named Entity Recognition (NER) | Ontonotes v5 (English) | HSCRF + softdict | F1 | 89.94 | #14 of 28 | 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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