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 2701–2750 of 2874 papers

TitleStatusHype
Federated Named Entity Recognition—0
FedNER: Privacy-preserving Medical Named Entity Recognition with Federated Learning—0
FEUP at SemEval-2018 Task 5: An Experimental Study of a Question Answering System—0
Few-Shot Class-Incremental Learning for Named Entity Recognition—0
Few-shot clinical entity recognition in English, French and Spanish: masked language models outperform generative model prompting—0
Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks—0
Few-shot learning for medical text: A systematic review—0
Few-shot Learning for Sumerian Named Entity Recognition—0
Few-Shot Named Entity Recognition: An Empirical Baseline Study—0
Few-Shot Named Entity Recognition with Biaffine Span Representation—0
Few-shot Named Entity Recognition with Cloze Questions—0
Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes—0
Few-shot Named Entity Recognition with Joint Token and Sentence Awareness—0
Few-shot Name Entity Recognition on StackOverflow—0
Few-Shot Nested Named Entity Recognition—0
Few-Shot Sequence Labeling with Label Dependency Transfer and Pair-wise Embedding—0
Fighting Against the Repetitive Training and Sample Dependency Problem in Few-shot Named Entity Recognition—0
FiLLM -- A Filipino-optimized Large Language Model based on Southeast Asia Large Language Model (SEALLM)—0
Finding Names in Trove: Named Entity Recognition for Australian Historical Newspapers—0
Finding the Right Supervisor: Expert-Finding in a University Domain—0
Fine-grained Location Extraction via Curriculum Learning—0
Finely Tuned, 2 Billion Token Based Word Embeddings for Portuguese—0
Fine-tuning for Named Entity Recognition Using Part-of-Speech Tagging—0
Fine Tuning Named Entity Extraction Models for the Fantasy Domain—0
Fine-tuning Pre-trained Named Entity Recognition Models For Indian Languages—0
FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets—0
Flat Multi-modal Interaction Transformer for Named Entity Recognition—0
FLERT: Document-Level Features for Named Entity Recognition—0
FlexNER: A Flexible LSTM-CNN Stack Framework for Named Entity Recognition—0
Flippi: End To End GenAI Assistant for E-Commerce—0
Floods Relevancy and Identification of Location from Twitter Posts using NLP Techniques—0
Flow Graph Corpus from Recipe Texts—0
Focused training sets to reduce noise in NER feature models—0
FRASIMED: a Clinical French Annotated Resource Produced through Crosslingual BERT-Based Annotation Projection—0
FRE at SocialDisNER: Joint Learning of Language Models for Named Entity Recognition—0
From DBpedia and WordNet hierarchies to LinkedIn and Twitter—0
From Dialogue to Diagram: Task and Relationship Extraction from Natural Language for Accelerated Business Process Prototyping—0
From ELTeC Text Collection Metadata and Named Entities to Linked-data (and Back)—0
From Textual Information Sources to Linked Data in the Agatha Project—0
From Voice to Safety: Language AI Powered Pilot-ATC Communication Understanding for Airport Surface Movement Collision Risk Assessment—0
Efficient Online ML API Selection for Multi-Label Classification Tasks—0
F-Score Driven Max Margin Neural Network for Named Entity Recognition in Chinese Social Media—0
FudanNLP: A Toolkit for Chinese Natural Language Processing—0
Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition—0
GAMedX: Generative AI-based Medical Entity Data Extractor Using Large Language Models—0
Gated Graph Sequence Neural Networks for Chinese Healthcare Named Entity Recognition—0
Gaussian Prior Reinforcement Learning for Nested Named Entity Recognition—0
Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition—0
GEIC: Universal and Multilingual Named Entity Recognition with Large Language Models—0
GEMNET: Effective Gated Gazetteer Representations for Recognizing Complex Entities in Low-context Input—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