Named Entity Recognition Ner On Ontonotes V5

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評価結果

このベンチマークにおける各モデルのパフォーマンス結果

		Paper Title	Repository
BERT-MRC+DSC	92.07	Dice Loss for Data-imbalanced NLP Tasks
PL-Marker	91.9	Packed Levitated Marker for Entity and Relation Extraction
Baseline + BS	91.74	Boundary Smoothing for Named Entity Recognition
Biaffine-NER	91.3	Named Entity Recognition as Dependency Parsing
BERT-MRC	91.11	A Unified MRC Framework for Named Entity Recognition
PIQN	90.96	Parallel Instance Query Network for Named Entity Recognition
HGN	90.92	Hero-Gang Neural Model For Named Entity Recognition
Syn-LSTM + BERT (wo doc-context)	90.85	Better Feature Integration for Named Entity Recognition
DiffusionNER	90.66	DiffusionNER: Boundary Diffusion for Named Entity Recognition
W2NER	90.50	Unified Named Entity Recognition as Word-Word Relation Classification
BARTNER	90.38	A Unified Generative Framework for Various NER Subtasks
AESINER	90.32	Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information
Hierarchical + BERT	90.30	Hierarchical Contextualized Representation for Named Entity Recognition
HSCRF + softdict	89.94	Towards Improving Neural Named Entity Recognition with Gazetteers	-
DGLSTM-CRF + ELMo	89.88	Dependency-Guided LSTM-CRF for Named Entity Recognition
NuNER	89.1	NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data
Syn-LSTM (wo doc-context)	89.04	Better Feature Integration for Named Entity Recognition
CVT + Multi-Task + Large	88.81	Semi-Supervised Sequence Modeling with Cross-View Training
DGLSTM-CRF (L=2)	88.52	Dependency-Guided LSTM-CRF for Named Entity Recognition
Att-BiLSTM-CNN	88.4	Why Attention? Analyze BiLSTM Deficiency and Its Remedies in the Case of NER

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Named Entity Recognition Ner On Ontonotes V5

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