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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 2201–2225 of 2874 papers

Title	Date	Tasks	Status
Vancouver Welcomes You! Minimalist Location Metonymy Resolution	Jul 1, 2017	named-entity-recognitionNamed Entity Recognition	CodeCode Available
Aggregating and Predicting Sequence Labels from Crowd Annotations	Jul 1, 2017	Articlesnamed-entity-recognition	CodeCode Available
A Local Detection Approach for Named Entity Recognition and Mention Detection	Jul 1, 2017	Feature EngineeringImage Classification	—Unverified
Detecting annotation noise in automatically labelled data	Jul 1, 2017	Active LearningDomain Adaptation	—Unverified
Context-Dependent Sentiment Analysis in User-Generated Videos	Jul 1, 2017	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available
Bayesian Modeling of Lexical Resources for Low-Resource Settings	Jul 1, 2017	Low Resource Named Entity Recognitionnamed-entity-recognition	—Unverified
Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition	Jun 29, 2017	BIG-bench Machine LearningClinical Concept Extraction	CodeCode Available
Named Entity Recognition with stack residual LSTM and trainable bias decoding	Jun 23, 2017	named-entity-recognitionNamed Entity Recognition	—Unverified
Recognizing irregular entities in biomedical text via deep neural networks	Jun 13, 2017	Feature Engineeringnamed-entity-recognition	—Unverified
Context encoders as a simple but powerful extension of word2vec	Jun 8, 2017	named-entity-recognitionNamed Entity Recognition	CodeCode Available
Exploration de traits pour la reconnaissance d'entit\'es nomm\'ees du Fran par apprentissage automatique (Feature exploration for French Named Entity Recognition with Machine Learning)	Jun 1, 2017	named-entity-recognitionNamed Entity Recognition	—Unverified
Morphological Embeddings for Named Entity Recognition in Morphologically Rich Languages	Jun 1, 2017	Morphological Analysisnamed-entity-recognition	—Unverified
The Importance of Automatic Syntactic Features in Vietnamese Named Entity Recognition	May 29, 2017	named-entity-recognitionNamed Entity Recognition	—Unverified
Second-Order Word Embeddings from Nearest Neighbor Topological Features	May 23, 2017	named-entity-recognitionNamed Entity Recognition	CodeCode Available
Softmax Q-Distribution Estimation for Structured Prediction: A Theoretical Interpretation for RAML	May 19, 2017	Dependency ParsingImage Captioning	—Unverified
Transfer Learning for Named-Entity Recognition with Neural Networks	May 17, 2017	De-identificationnamed-entity-recognition	—Unverified
NeuroNER: an easy-to-use program for named-entity recognition based on neural networks	May 16, 2017	named-entity-recognitionNamed Entity Recognition	CodeCode Available
End-to-end Recurrent Neural Network Models for Vietnamese Named Entity Recognition: Word-level vs. Character-level	May 11, 2017	named-entity-recognitionNamed Entity Recognition	CodeCode Available
Entity Linking with people entity on Wikipedia	May 2, 2017	coreference-resolutionCoreference Resolution	—Unverified
Improving Optical Character Recognition of Finnish Historical Newspapers with a Combination of Fraktur \& Antiqua Models and Image Preprocessing	May 1, 2017	Boundary DetectionInformation Retrieval	—Unverified
Optimizing a PoS Tagset for Norwegian Dependency Parsing	May 1, 2017	Dependency ParsingFeature Engineering	—Unverified
Docforia: A Multilayer Document Model	May 1, 2017	Coreference ResolutionDependency Parsing	—Unverified
The Effect of Excluding Out of Domain Training Data from Supervised Named-Entity Recognition	May 1, 2017	named-entity-recognitionNamed Entity Recognition	—Unverified
Tagging Named Entities in 19th Century and Modern Finnish Newspaper Material with a Finnish Semantic Tagger	May 1, 2017	Named Entity Recognition (NER)Optical Character Recognition (OCR)	—Unverified
A Multilingual Entity Linker Using PageRank and Semantic Graphs	May 1, 2017	Entity LinkingNamed 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