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 1151–1200 of 2874 papers

TitleStatusHype
A visual search engine for Bangladeshi laws—0
ANEC: An Amharic Named Entity Corpus and Transformer Based Recognizer—0
Dependency parsing representation effects on the accuracy of semantic applications --- an example of an inflective language—0
Dependency Based Embeddings for Sentence Classification Tasks—0
Auxiliary Sequence Labeling Tasks for Disfluency Detection—0
Dependency-Aware Named Entity Recognition with Relative and Global Attentions—0
Auxiliary Learning for Named Entity Recognition with Multiple Auxiliary Biomedical Training Data—0
An Easy-to-use and Robust Approach for the Differentially Private De-Identification of Clinical Textual Documents—0
Adversarial Learning for Chinese NER from Crowd Annotations—0
DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT—0
Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition—0
AutoTriggER: Named Entity Recognition with Auxiliary Trigger Extraction—0
Delexicalized Paraphrase Generation—0
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction—0
De-identifying Australian Hospital Discharge Summaries: An End-to-End Framework using Ensemble of Deep Learning Models—0
De-identification of Unstructured Clinical Texts from Sequence to Sequence Perspective—0
Deformable Stacked Structure for Named Entity Recognition—0
Automatic Textual Evidence Mining in COVID-19 Literature—0
Adversarial Evaluation of BERT for Biomedical Named Entity Recognition—0
A Corpus of Tables in Full-Text Biomedical Research Publications—0
A Beam-Search Decoder for Normalization of Social Media Text with Application to Machine Translation—0
DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature—0
DeepTagger: Knowledge Enhanced Named Entity Recognition for Web-Based Ads Queries—0
Automatic Punjabi Text Extractive Summarization System—0
Automatic lexical semantic classification of nouns—0
ANCHOLIK-NER: A Benchmark Dataset for Bangla Regional Named Entity Recognition—0
DeepPavlov: Open-Source Library for Dialogue Systems—0
DeepNNNER: Applying BLSTM-CNNs and Extended Lexicons to Named Entity Recognition in Tweets—0
Automatic Keyword Extraction on Twitter—0
Deep neural networks for fine-grained surveillance of overdose mortality—0
Deep neural model with enhanced embeddings for pharmaceutical and chemical entities recognition in Spanish clinical text—0
Automatic Identification of Expressions of Locations in Tweet Messages using Conditional Random Fields—0
An Automated Domain Understanding Technique for Knowledge Graph Generation—0
Deep Neural Model Inspection and Comparison via Functional Neuron Pathways—0
Deep Neural Model for Manipuri Multiword Named Entity Recognition with Unsupervised Cluster Feature—0
Deep Multilingual Correlation for Improved Word Embeddings—0
Deep Learning with Minimal Training Data: TurkuNLP Entry in the BioNLP Shared Task 2016—0
Automatic Extraction of Medication Names in Tweets as Named Entity Recognition—0
Deep Learning Transformer Architecture for Named Entity Recognition on Low Resourced Languages: State of the art results—0
Deep learning models for representing out-of-vocabulary words—0
Automatic Extraction of Entities and Relation from Legal Documents—0
Deep Learning Models For Multiword Expression Identification—0
Deep Learning for NLP (without Magic)—0
Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations—0
An automated domain-independent text reading, interpreting and extracting approach for reviewing the scientific literature—0
A Natural Language Processing Pipeline of Chinese Free-text Radiology Reports for Liver Cancer Diagnosis—0
Deep Learning Architecture for Patient Data De-identification in Clinical Records—0
Deep Learning Approach for Intelligent Named Entity Recognition of Cyber Security—0
Automatic Creation of Arabic Named Entity Annotated Corpus Using Wikipedia—0
Deep Learning Approaches for Extracting Adverse Events and Indications of Dietary Supplements from Clinical Text—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