Nested Named Entity Recognition On Ace 2004

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이 벤치마크에서 각 모델의 성능 결과

모델 이름	F1	Paper Title	Repository
BINDER	88.7	Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning	-
PIQN	88.14	Parallel Instance Query Network for Named Entity Recognition	-
Triaffine + ALBERT	88.56	Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition	-
W2NER	87.52	Unified Named Entity Recognition as Word-Word Relation Classification	-
CNN-NER	88.03	An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition	-
Second-best learning and decoding + BERT + Flair	85.82	Nested Named Entity Recognition via Second-best Sequence Learning and Decoding	-
seq2seq+BERT+Flair	84.40	Neural Architectures for Nested NER through Linearization	-
Baseline + BS	87.98	Boundary Smoothing for Named Entity Recognition	-
PromptNER [RoBERTa-large]	88.72	PromptNER: Prompt Locating and Typing for Named Entity Recognition	-
Second-best learning and decoding + BERT	84.97	Nested Named Entity Recognition via Second-best Sequence Learning and Decoding	-
Triaffine + BioBERT	87.40	Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition	-
LogSumExpDecoder	86.42	Nested Named Entity Recognition via Explicitly Excluding the Influence of the Best Path
PO-TreeCRFs	86.6	Nested Named Entity Recognition with Partially-Observed TreeCRFs	-
BoningKnife	86.41	BoningKnife: Joint Entity Mention Detection and Typing for Nested NER via prior Boundary Knowledge	-
MGNER	79.5	Multi-Grained Named Entity Recognition	-
Second-best learning and decoding	77.44	Nested Named Entity Recognition via Second-best Sequence Learning and Decoding	-
Hashing	87.93	To be Continuous, or to be Discrete, Those are Bits of Questions	-
Locate and Label	87.41	Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition	-
DiffusionNER	88.39	DiffusionNER: Boundary Diffusion for Named Entity Recognition	-
BARTNER	86.84	A Unified Generative Framework for Various NER Subtasks	-

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