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

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Showing 1951–2000 of 2874 papers

TitleStatusHype
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition—0
A Comparison and Improvement of Online Learning Algorithms for Sequence Labeling—0
A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings—0
A Compositional-Distributional Semantic Model for Searching Complex Entity Categories—0
A Compositional Interpretation of Biomedical Event Factuality—0
A Comprehensive Analysis of Acknowledgement Texts in Web of Science: a case study on four scientific domains—0
A Comprehensive Study of Gender Bias in Chemical Named Entity Recognition Models—0
A Concrete Chinese NLP Pipeline—0
A Continuous Relaxation of Beam Search for End-to-end Training of Neural Sequence Models—0
A Corpus of Tables in Full-Text Biomedical Research Publications—0
A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products—0
A Cost Sensitive Part-of-Speech Tagging: Differentiating Serious Errors from Minor Errors—0
A Crowdsourcing Approach for Annotating Causal Relation Instances in Wikipedia—0
Active Curriculum Learning—0
Active Defense Against Social Engineering: The Case for Human Language Technology—0
Active Learning for Coreference Resolution—0
Active Learning for Coreference Resolution—0
Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion—0
Active Learning with Tabular Language Models—0
AdaK-NER: An Adaptive Top-K Approach for Named Entity Recognition with Incomplete Annotations—0
Adaptation d'un syst\`eme de reconnaissance d'entit\'es nomm\'ees pour le fran \`a l'anglais \`a moindre co\^ut (Adapting a French Named Entity Recognition System to English with Minimal Costs) [in French]—0
Adaptation of Deep Bidirectional Transformers for Afrikaans Language—0
Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning—0
Adapting Discriminative Reranking to Grounded Language Learning—0
Adapting Freely Available Resources to Build an Opinion Mining Pipeline in Portuguese—0
Adapting Language Specific Components of Cross-Media Analysis Frameworks to Less-Resourced Languages: the Case of Amharic—0
Adapting the TTL Romanian POS Tagger to the Biomedical Domain—0
Adapting word2vec to Named Entity Recognition—0
Adaptive Name Entity Recognition under Highly Unbalanced Data—0
Adaptive Self-training for Few-shot Neural Sequence Labeling—0
Adaptive Self-training for Neural Sequence Labeling with Few Labels—0
Adaptive Threshold Selective Self-Attention for Chinese NER—0
A data and analysis resource for an experiment in text mining a collection of micro-blogs on a political topic.—0
A Data-driven Approach to Named Entity Recognition for Early Modern French—0
A Dataset for ICD-10 Coding of Death Certificates: Creation and Usage—0
A Deeper Study on Features for Named Entity Recognition—0
A Deep Learning approach for Hindi Named Entity Recognition—0
A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot—0
A Deep Learning-Based System for PharmaCoNER—0
A Deep Neural Network based Approach for Entity Extraction in Code-Mixed Indian Social Media Text—0
A dictionary- and rule-based system for identification of bacteria and habitats in text—0
A disambiguation resource extracted from Wikipedia for semantic annotation—0
A Domain Adaptation Regularization for Denoising Autoencoders—0
A Domain and Language Independent Named Entity Classification Approach Based on Profiles and Local Information—0
A Dual-Attention Network for Joint Named Entity Recognition and Sentence Classification of Adverse Drug Events—0
Advanced Visual Analytics Methods for Literature Analysis—0
Adversarial Evaluation of BERT for Biomedical Named Entity Recognition—0
Adversarial Learning for Chinese NER from Crowd Annotations—0
Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER—0
Adversarial Structured Prediction for Multivariate Measures—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