Named Entity Recognition Ner On Conll 2003

Metrics

Results

Performance results of various models on this benchmark

Model Name	F1	Paper Title	Repository
Bi-LSTM-CNN	91.62	Named Entity Recognition with Bidirectional LSTM-CNNs	-
PromptNER [BERT-large]	92.41	PromptNER: Prompt Locating and Typing for Named Entity Recognition	-
Bi-LSTM-CNN-CRF	91.22	A Deep Neural Network Model for the Task of Named Entity Recognition
LM-LSTM-CRF	91.24	Empower Sequence Labeling with Task-Aware Neural Language Model	-
RoBERTa + SubRegWeigh (K-means)	93.81	SubRegWeigh: Effective and Efficient Annotation Weighing with Subword Regularization	-
LUKE + SubRegWeigh (K-means)	94.2	SubRegWeigh: Effective and Efficient Annotation Weighing with Subword Regularization	-
BERT-CRF	93.6	Focusing on Potential Named Entities During Active Label Acquisition	-
IntNet + BiLSTM-CRF	91.64	Learning Better Internal Structure of Words for Sequence Labeling	-
Yang et al. ([2017a])	91.62	Neural Reranking for Named Entity Recognition	-
Yang et al.	91.26	Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks	-
CNN Large + fine-tune	93.5	Cloze-driven Pretraining of Self-attention Networks	-
Neural-CRF+AE	92.29	Evaluating the Utility of Hand-crafted Features in Sequence Labelling	-
CVT + Multi-Task + Large	92.61	Semi-Supervised Sequence Modeling with Cross-View Training	-
PRISM	91.8	A Prism Module for Semantic Disentanglement in Name Entity Recognition	-
XLNet	93.28	Named entity recognition architecture combining contextual and global features	-
XLM-RoBERTa-large union	93.69	Transformer-based Named Entity Recognition with Combined Data Representation	-
BLSTM-CNN-CRF	91.21	End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF	-
SpanRel	92.2	Generalizing Natural Language Analysis through Span-relation Representations	-
GoLLIE	93.1	GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction	-
Locate and Label	92.94	Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition	-

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