SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2001–2050 of 2874 papers

TitleStatusHype
A dynamic programming algorithm for span-based nested named-entity recognition in O(n^2)—0
A Feature-based Ensemble Approach to Recognition of Emerging and Rare Named Entities—0
A fine-grained corpus annotation schema of German nephrology records—0
A Framework for Developing and Evaluating Word Embeddings of Drug-named Entity—0
A Framework for Procedural Text Understanding—0
A French Corpus and Annotation Schema for Named Entity Recognition and Relation Extraction of Financial News—0
A Gazetteer and Georeferencing for Historical English Documents—0
A General-Purpose Annotation Model for Knowledge Discovery: Case Study in Spanish Clinical Text—0
A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events—0
Aggregated Semantic Matching for Short Text Entity Linking—0
Aggregating User-Centric and Post-Centric Sentiments from Social Media for Topical Stance Prediction—0
A Graph-based Text Similarity Measure That Employs Named Entity Information—0
A Hassle-Free Machine Learning Method for Cohort Selection of Clinical Trials—0
A Hybrid Approach to Features Representation for Fine-grained Arabic Named Entity Recognition—0
A Hybrid Approach to Finding Phenotype Candidates in Genetic Texts—0
A hybrid deep-learning approach for complex biochemical named entity recognition—0
A Hybrid Statistical Approach for Named Entity Recognition for Malayalam Language—0
A Hybrid Stepwise Approach for De-identifying Person Names in Clinical Documents—0
AI-assisted Knowledge Discovery in Biomedical Literature to Support Decision-making in Precision Oncology—0
AI-KU at SemEval-2016 Task 11: Word Embeddings and Substring Features for Complex Word Identification—0
A New Multifractal-based Deep Learning Model for Text Mining—0
A Japanese Chess Commentary Corpus—0
A Joint Chinese Named Entity Recognition and Disambiguation System—0
A Joint Model for Entity Analysis: Coreference, Typing, and Linking—0
A Joint Named Entity Recognition and Entity Linking System—0
A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy—0
Aleda, a free large-scale entity database for French—0
A Legal Perspective on Training Models for Natural Language Processing—0
A Lexicon-Based Graph Neural Network for Chinese NER—0
Aligning Opinions: Cross-Lingual Opinion Mining with Dependencies—0
A Local Detection Approach for Named Entity Recognition and Mention Detection—0
AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging—0
A Machine Learning Approach for Phenotype Name Recognition—0
A Marker-based Neural Network System for Extracting Social Determinants of Health—0
A Method to Generate a Machine-Labeled Data for Biomedical Named Entity Recognition with Various Sub-Domains—0
A Mixed Morpho-Syntactic and Statistical Approach to Chinese Named Entity Recognition (Une approche mixte morpho-syntaxique et statistique pour la reconnaissance d'entit\'es nomm\'ees en langue chinoise) [in French]—0
A More Efficient Chinese Named Entity Recognition base on BERT and Syntactic Analysis—0
A Morpho-Syntactically Informed LSTM-CRF Model for Named Entity Recognition—0
A multi-constraint structured hinge loss for named-entity recognition—0
A Multi-Gate Encoder for Joint Entity and Relation Extraction—0
A Multilingual Entity Linker Using PageRank and Semantic Graphs—0
A Multilingual Evaluation of NER Robustness to Adversarial Inputs—0
A multimodal deep learning approach for named entity recognition from social media—0
A Multi-Strategy Approach for Location Mining in Tweets: AUT NLP Group Entry for ALTA-2014 Shared Task—0
A Multi-task Learning Approach for Named Entity Recognition using Local Detection—0
A Multi-task Learning Approach to Adapting Bilingual Word Embeddings for Cross-lingual Named Entity Recognition—0
A Multi-Task Learning Framework for Extracting Drugs and Their Interactions from Drug Labels—0
An Adaptive Framework for Named Entity Combination—0
Analysing recall loss in named entity slot filling—0
Analysis of Named Entity Recognition and Linking for Tweets—0
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All datasetsCoNLL 2003 (English)Ontonotes v5 (English)NCBI DiseaseWNUT 2017ACE 2005JNLPBABC5CDRGENIABC2GMBC5CDR-chemicalSLUECoNLL++

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ACE + document-contextF194.6—Unverified
2LUKE 483MF194.3—Unverified
3Co-regularized LUKEF194.22—Unverified
4LUKE + SubRegWeigh (K-means)F194.2—Unverified
5ASP+T5-3BF194.1—Unverified
6FLERT XLM-RF194.09—Unverified
7PL-MarkerF194—Unverified
8CL-KLF193.85—Unverified
9XLNet-GCNF193.82—Unverified
10RoBERTa + SubRegWeigh (K-means)F193.81—Unverified
#ModelMetricClaimedVerifiedStatus
1BERT-MRC+DSCF192.07—Unverified
2PL-MarkerF191.9—Unverified
3Baseline + BSF191.74—Unverified
4Biaffine-NERF191.3—Unverified
5BERT-MRCF191.11—Unverified
6PIQNF190.96—Unverified
7HGNF190.92—Unverified
8Syn-LSTM + BERT (wo doc-context)F190.85—Unverified
9DiffusionNERF190.66—Unverified
10W2NERF190.5—Unverified
#ModelMetricClaimedVerifiedStatus
1BioBERTF189.71—Unverified
2SpanModel + SequenceLabelingModelF189.6—Unverified
3SciFive-BaseF189.39—Unverified
4Spark NLPF189.13—Unverified
5BLSTM-CNN-Char (SparkNLP)F189.13—Unverified
6KeBioLMF189.1—Unverified
7CL-KLF188.96—Unverified
8BioKMNER + BioBERTF188.77—Unverified
9BioLinkBERT (large)F188.76—Unverified
10CompactBioBERTF188.67—Unverified
#ModelMetricClaimedVerifiedStatus
1CL-KLF160.45—Unverified
2RoBERTa + SubRegWeigh (K-means)F160.29—Unverified
3BERT-CRF (Replicated in AdaSeq)F159.69—Unverified
4RoBERTa-BiLSTM-contextF159.61—Unverified
5BERT + RegLERF158.9—Unverified
6TNER -xlm-r-largeF158.5—Unverified
7HGNF157.41—Unverified
8ASA + RoBERTaF157.3—Unverified
9BERTweetF156.5—Unverified
10MINERF154.86—Unverified
#ModelMetricClaimedVerifiedStatus
1Ours: cross-sentence ALBF190.9—Unverified
2GoLLIEF189.6—Unverified
3PromptNER [RoBERTa-large]F188.26—Unverified
4PIQNF187.42—Unverified
5PromptNER [BERT-large]F187.21—Unverified
6DiffusionNERF186.93—Unverified
7BERT-MRCF186.88—Unverified
8UniNER-7BF186.69—Unverified
9Locate and LabelF186.67—Unverified
10BoningKnifeF185.46—Unverified
#ModelMetricClaimedVerifiedStatus
1KeBioLMF182—Unverified
2BLSTM-CNN-Char (SparkNLP)F181.29—Unverified
3Spark NLPF181.29—Unverified
4BINDERF180.3—Unverified
5BioMobileBERTF180.13—Unverified
6BioLinkBERT (large)F180.06—Unverified
7DistilBioBERTF179.97—Unverified
8CompactBioBERTF179.88—Unverified
9BioDistilBERTF179.1—Unverified
10PubMedBERT uncasedF179.1—Unverified
#ModelMetricClaimedVerifiedStatus
1BINDERF191.9—Unverified
2ConNERF191.3—Unverified
3CL-L2F190.99—Unverified
4aimpedF190.95—Unverified
5BertForTokenClassification (Spark NLP)F190.89—Unverified
6BioLinkBERT (large)F190.22—Unverified
7ELECTRAMedF190.03—Unverified
8BLSTM-CNN-Char (SparkNLP)F189.73—Unverified
9Spark NLPF189.73—Unverified
10UniNER-7BF189.34—Unverified