Research map / Large language models / Retrieval and knowledge
OTyper: A Neural Architecture for Open Named Entity Typing
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add entity-type pattern features (ablation) | OTyper · FIGER(GOLD) unseen types | weighted AUC-ROC: 0.842 -> 0.870 | Helped |
| add type-only pattern features (ablation) | OTyper · FIGER(GOLD) unseen types | weighted AUC-ROC: 0.848 -> 0.870 | Helped |
| add mention features (ablation) | OTyper · FIGER(GOLD) unseen types | weighted AUC-ROC: 0.863 -> 0.870 | No clear effect |
| remove 3 most similar training types (ablation) | OTyper · FIGER(GOLD) held-out types | AUC-ROC: 0.869 -> 0.798 | Hurt |
| remove 3 least similar training types (ablation) | OTyper · FIGER(GOLD) held-out types | AUC-ROC: 0.869 -> 0.866 | No clear effect |
| add pattern features on top of mention features (supervised setting) (ablation) | OTyper · FIGER(GOLD) supervised | strict accuracy: 0.584 -> 0.595 | Helped |
| use OTyper type-embedding architecture instead of NFGEC in supervised setting | OTyper · FIGER(GOLD) supervised | strict accuracy: 0.597 -> 0.595 | No clear effect |
| OTyper vs pattern baseline on unseen types | OTyper · FIGER(GOLD) unseen types | weighted AUC-ROC: 0.639 -> 0.870 | Helped |
Closest papers by meaning
- UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition (ICLR 2024 · similarity 0.87)
- Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains (AAAI 2024 · similarity 0.86)
- Small Language Model Makes an Effective Long Text Extractor (AAAI 2025 · similarity 0.85)
- MCL-NER: Cross-Lingual Named Entity Recognition via Multi-View Contrastive Learning (AAAI 2024 · similarity 0.84)
- GuideNER: Annotation Guidelines Are Better than Examples for In-Context Named Entity Recognition (AAAI 2025 · similarity 0.84)
- Span Graph Transformer for Document-Level Named Entity Recognition (AAAI 2024 · similarity 0.84)
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