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 426–450 of 2874 papers

Title	Date	Tasks	Status
A Framework for Procedural Text Understanding	Jul 1, 2015	Named Entity Recognition (NER)Procedural Text Understanding	—Unverified
A New Concept of Deep Reinforcement Learning based Augmented General Tagging System	Aug 1, 2018	Deep Reinforcement LearningNamed Entity Recognition (NER)	—Unverified
A Framework for Developing and Evaluating Word Embeddings of Drug-named Entity	Jul 1, 2018	Named Entity Recognition (NER)Outlier Detection	—Unverified
Active Learning for Coreference Resolution	Jun 1, 2012	Active Learningcoreference-resolution	—Unverified
Accelerated materials language processing enabled by GPT	Aug 18, 2023	Document ClassificationExtractive Question-Answering	—Unverified
A Benchmark of Nested Named Entity Recognition Approaches in Historical Structured Documents	Feb 20, 2023	named-entity-recognitionNamed Entity Recognition	—Unverified
Building OCR/NER Test Collections	May 1, 2020	named-entity-recognitionNamed Entity Recognition	—Unverified
An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition	May 14, 2020	named-entity-recognitionNamed Entity Recognition	—Unverified
A Neural Transition-based Joint Model for Disease Named Entity Recognition and Normalization	Aug 1, 2021	Multi-Task Learningnamed-entity-recognition	—Unverified
Active Learning for Coreference Resolution	Jun 1, 2012	Active Learningcoreference-resolution	—Unverified
A Neural Pipeline Approach for the PharmaCoNER Shared Task using Contextual Exhaustive Models	Nov 1, 2019	Entity Embeddingsnamed-entity-recognition	—Unverified
Building Hierarchically Disentangled Language Models for Text Generation with Named Entities	Dec 1, 2020	Dependency ParsingLanguage Modeling	—Unverified
A fine-grained corpus annotation schema of German nephrology records	Dec 1, 2016	BIG-bench Machine LearningNamed Entity Recognition (NER)	—Unverified
Active Defense Against Social Engineering: The Case for Human Language Technology	May 1, 2020	AttributeDialogue Generation	—Unverified
Building Lexical Vector Representations from Concept Definitions	Apr 1, 2017	Dependency ParsingMachine Translation	—Unverified
Building Low-Resource NER Models Using Non-Speaker Annotation	Jun 17, 2020	Low Resource Named Entity Recognitionnamed-entity-recognition	—Unverified
Building and Evaluating Universal Named-Entity Recognition English corpus	Dec 14, 2022	named-entity-recognitionNamed Entity Recognition	—Unverified
Building astroBERT, a language model for Astronomy & Astrophysics	Dec 1, 2021	AstronomyLanguage Modeling	—Unverified
Active Curriculum Learning	Aug 1, 2021	Active LearningInformativeness	—Unverified
A Case Study on the Importance of Named Entities in a Machine Translation Pipeline for Customer Support Content	Jun 1, 2022	Machine Translationnamed-entity-recognition	—Unverified
Building English-Vietnamese Named Entity Corpus with Aligned Bilingual News Articles	Aug 1, 2014	ArticlesInformation Retrieval	—Unverified
An End-to-End Solution for Named Entity Recognition in eCommerce Search	Dec 11, 2020	named-entity-recognitionNamed Entity Recognition	—Unverified
A Feature-based Ensemble Approach to Recognition of Emerging and Rare Named Entities	Sep 1, 2017	Entity Extraction using GANnamed-entity-recognition	—Unverified
BENTO: A Visual Platform for Building Clinical NLP Pipelines Based on CodaLab	Jul 1, 2020	Managementnamed-entity-recognition	—Unverified
A Crowdsourcing Approach for Annotating Causal Relation Instances in Wikipedia	Nov 1, 2017	Named Entity Recognition (NER)Question Answering	—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