Named Entity Recognition (NER)

Named Entity Recognition (NER) is a task of Natural Language Processing (NLP) that involves identifying and classifying named entities in a text into predefined categories such as person names, organizations, locations, and others. The goal of NER is to extract structured information from unstructured text data and represent it in a machine-readable format. Approaches typically use BIO notation, which differentiates the beginning (B) and the inside (I) of entities. O is used for non-entity tokens.

Example:

| Mark | Watney | visited | Mars | | --- | ---| --- | --- | | B-PER | I-PER | O | B-LOC |

( Image credit: Zalando )

Papers

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Showing 1101–1125 of 2874 papers

Title	Date	Tasks	Status
Characterizing Misclassifications of Deep NLP Models	Mar 12, 2021	named-entity-recognitionNamed Entity Recognition	—Unverified
A PubMedBERT-based Classifier with Data Augmentation Strategy for Detecting Medication Mentions in Tweets	Nov 3, 2021	Data AugmentationManagement	—Unverified
Character-based Bidirectional LSTM-CRF with words and characters for Japanese Named Entity Recognition	Sep 1, 2017	named-entity-recognitionNamed Entity Recognition	—Unverified
Character-Aware Neural Networks for Arabic Named Entity Recognition for Social Media	Dec 1, 2016	Feature EngineeringInformation Retrieval	—Unverified
A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition	Feb 27, 2023	Contrastive Learningfew-shot-ner	—Unverified
A New Multifractal-based Deep Learning Model for Text Mining	Nov 27, 2021	Chinese Named Entity RecognitionDeep Learning	—Unverified
Challenges in the Knowledge Base Population Slot Filling Task	May 1, 2012	DiversityEntity Linking	—Unverified
Challenges in clinical natural language processing for automated disorder normalization	Jul 14, 2015	Learning-To-RankMedical Named Entity Recognition	—Unverified
A Comparative Analysis of Word Segmentation, Part-of-Speech Tagging, and Named Entity Recognition for Historical Chinese Sources, 1900-1950	Mar 25, 2025	named-entity-recognitionNamed Entity Recognition	—Unverified
Challenges and Solutions for Latin Named Entity Recognition	Dec 1, 2016	Active LearningDomain Adaptation	—Unverified
Centroids: Gold standards with distributional variation	May 1, 2012	Named Entity Recognition (NER)	—Unverified
Few-Shot Named Entity Recognition with Biaffine Span Representation	Nov 16, 2021	Few-shot NERNamed Entity Recognition	—Unverified
Few-shot Named Entity Recognition with Cloze Questions	Nov 24, 2021	Few-Shot Learningnamed-entity-recognition	—Unverified
CELI: An Experiment with Cross Language Textual Entailment	Jul 1, 2012	LemmatizationNamed Entity Recognition (NER)	—Unverified
AI-KU at SemEval-2016 Task 11: Word Embeddings and Substring Features for Complex Word Identification	Jun 1, 2016	Complex Word IdentificationDocument Classification	—Unverified
10,000+ Times Accelerated Robust Subset Selection (ARSS)	Sep 12, 2014	10-shot image generationAction Recognition	—Unverified
CDJUR-BR -- A Golden Collection of Legal Document from Brazilian Justice with Fine-Grained Named Entities	May 20, 2023	AttributeJurisprudence	—Unverified
CCS Explorer: Relevance Prediction, Extractive Summarization, and Named Entity Recognition from Clinical Cohort Studies	Nov 1, 2022	ArticlesDrug Discovery	—Unverified
A practical method for occupational skills detection in Vietnamese job listings	Oct 26, 2022	named-entity-recognitionNamed Entity Recognition	—Unverified
CBLUE: A Chinese Biomedical Language Understanding EvaluationBenchmark	Nov 16, 2021	named-entity-recognitionNamed Entity Recognition	—Unverified
Applying unsupervised keyphrase methods on concepts extracted from discharge sheets	Mar 15, 2023	Binary ClassificationEntity Linking	—Unverified
Causal Interventions-based Few-Shot Named Entity Recognition	May 3, 2023	Few-Shot Learningfew-shot-ner	—Unverified
Causal Distillation for Language Models	Jan 16, 2022	Language ModelingLanguage Modelling	—Unverified
Adapting word2vec to Named Entity Recognition	May 1, 2015	Dependency ParsingInformation Retrieval	—Unverified
A Boosted Semi-Markov Perceptron	Aug 1, 2013	ChunkingNamed Entity Recognition (NER)	—Unverified

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All datasets CoNLL 2003 (English)Ontonotes v5 (English)NCBI Disease WNUT 2017 ACE 2005 JNLPBA BC5CDR GENIA BC2GM BC5CDR-chemical SLUE CoNLL++

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ACE + document-context	F1	94.6	—	Unverified
2	LUKE 483M	F1	94.3	—	Unverified
3	Co-regularized LUKE	F1	94.22	—	Unverified
4	LUKE + SubRegWeigh (K-means)	F1	94.2	—	Unverified
5	ASP+T5-3B	F1	94.1	—	Unverified
6	FLERT XLM-R	F1	94.09	—	Unverified
7	PL-Marker	F1	94	—	Unverified
8	CL-KL	F1	93.85	—	Unverified
9	XLNet-GCN	F1	93.82	—	Unverified
10	RoBERTa + SubRegWeigh (K-means)	F1	93.81	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BERT-MRC+DSC	F1	92.07	—	Unverified
2	PL-Marker	F1	91.9	—	Unverified
3	Baseline + BS	F1	91.74	—	Unverified
4	Biaffine-NER	F1	91.3	—	Unverified
5	BERT-MRC	F1	91.11	—	Unverified
6	PIQN	F1	90.96	—	Unverified
7	HGN	F1	90.92	—	Unverified
8	Syn-LSTM + BERT (wo doc-context)	F1	90.85	—	Unverified
9	DiffusionNER	F1	90.66	—	Unverified
10	W2NER	F1	90.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BioBERT	F1	89.71	—	Unverified
2	SpanModel + SequenceLabelingModel	F1	89.6	—	Unverified
3	SciFive-Base	F1	89.39	—	Unverified
4	Spark NLP	F1	89.13	—	Unverified
5	BLSTM-CNN-Char (SparkNLP)	F1	89.13	—	Unverified
6	KeBioLM	F1	89.1	—	Unverified
7	CL-KL	F1	88.96	—	Unverified
8	BioKMNER + BioBERT	F1	88.77	—	Unverified
9	BioLinkBERT (large)	F1	88.76	—	Unverified
10	CompactBioBERT	F1	88.67	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CL-KL	F1	60.45	—	Unverified
2	RoBERTa + SubRegWeigh (K-means)	F1	60.29	—	Unverified
3	BERT-CRF (Replicated in AdaSeq)	F1	59.69	—	Unverified
4	RoBERTa-BiLSTM-context	F1	59.61	—	Unverified
5	BERT + RegLER	F1	58.9	—	Unverified
6	TNER -xlm-r-large	F1	58.5	—	Unverified
7	HGN	F1	57.41	—	Unverified
8	ASA + RoBERTa	F1	57.3	—	Unverified
9	BERTweet	F1	56.5	—	Unverified
10	MINER	F1	54.86	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Ours: cross-sentence ALB	F1	90.9	—	Unverified
2	GoLLIE	F1	89.6	—	Unverified
3	PromptNER [RoBERTa-large]	F1	88.26	—	Unverified
4	PIQN	F1	87.42	—	Unverified
5	PromptNER [BERT-large]	F1	87.21	—	Unverified
6	DiffusionNER	F1	86.93	—	Unverified
7	BERT-MRC	F1	86.88	—	Unverified
8	UniNER-7B	F1	86.69	—	Unverified
9	Locate and Label	F1	86.67	—	Unverified
10	BoningKnife	F1	85.46	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	KeBioLM	F1	82	—	Unverified
2	BLSTM-CNN-Char (SparkNLP)	F1	81.29	—	Unverified
3	Spark NLP	F1	81.29	—	Unverified
4	BINDER	F1	80.3	—	Unverified
5	BioMobileBERT	F1	80.13	—	Unverified
6	BioLinkBERT (large)	F1	80.06	—	Unverified
7	DistilBioBERT	F1	79.97	—	Unverified
8	CompactBioBERT	F1	79.88	—	Unverified
9	BioDistilBERT	F1	79.1	—	Unverified
10	PubMedBERT uncased	F1	79.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BINDER	F1	91.9	—	Unverified
2	ConNER	F1	91.3	—	Unverified
3	CL-L2	F1	90.99	—	Unverified
4	aimped	F1	90.95	—	Unverified
5	BertForTokenClassification (Spark NLP)	F1	90.89	—	Unverified
6	BioLinkBERT (large)	F1	90.22	—	Unverified
7	ELECTRAMed	F1	90.03	—	Unverified
8	BLSTM-CNN-Char (SparkNLP)	F1	89.73	—	Unverified
9	Spark NLP	F1	89.73	—	Unverified
10	UniNER-7B	F1	89.34	—	Unverified