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 2001–2050 of 2874 papers

TitleStatusHype
Language and Domain Independent Entity Linking with Quantified Collective Validation—0
Language Clustering for Multilingual Named Entity Recognition—0
Language Identification and Named Entity Recognition in Hinglish Code Mixed Tweets—0
Language Independent Named Entity Identification using Wikipedia—0
Language-Independent Named Entity Analysis Using Parallel Projection and Rule-Based Disambiguation—0
Language Models sounds the Death Knell of Knowledge Graphs—0
Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All—0
Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition—0
Large Sequence Representation Learning via Multi-Stage Latent Transformers—0
LasigeBioTM at MEDIQA 2019: Biomedical Question Answering using Bidirectional Transformers and Named Entity Recognition—0
Lasige-BioTM at ProfNER: BiLSTM-CRF and contextual Spanish embeddings for Named Entity Recognition and Tweet Binary Classification—0
LASIGE: using Conditional Random Fields and ChEBI ontology—0
Last Words: What Can Be Accomplished with the State of the Art in Information Extraction? A Personal View—0
Latvian and Lithuanian Named Entity Recognition with TildeNER—0
LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding—0
LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation—0
Lean Question Answering over Freebase from Scratch—0
Learn and Review: Enhancing Continual Named Entity Recognition via Reviewing Synthetic Samples—0
Learning a Product of Experts with Elitist Lasso—0
Learning Architectures from an Extended Search Space for Language Modeling—0
Learning Better Internal Structure of Words for Sequence Labeling—0
Learning Categories and their Instances by Contextual Features—0
Learning Dictionaries for Named Entity Recognition using Minimal Supervision—0
Learning Distributed Word Representations For Bidirectional LSTM Recurrent Neural Network—0
Learning Domain-Specific Word Embeddings from Sparse Cybersecurity Texts—0
Learning Dynamic Feature Selection for Fast Sequential Prediction—0
Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework—0
Learning Hidden Markov Models with Distributed State Representations for Domain Adaptation—0
Learning Lexical Embeddings with Syntactic and Lexicographic Knowledge—0
Learning Multilingual Meta-Embeddings for Code-Switching Named Entity Recognition—0
Learning Orthographic Features in Bi-directional LSTM for Biomedical Named Entity Recognition—0
Learning Personalized User Preference from Cold Start in Multi-turn Conversations—0
Learning Representations for Text-level Discourse Parsing—0
Learning Rich Representation of Keyphrases from Text—0
Learning Robust Named Entity Recognizers From Noisy Data With Retrieval Augmentation—0
Learning Sense-specific Word Embeddings By Exploiting Bilingual Resources—0
Learning Structured Perceptrons for Coreference Resolution with Latent Antecedents and Non-local Features—0
Learning the Structure of Variable-Order CRFs: a finite-state perspective—0
Learning to Capitalize with Character-Level Recurrent Neural Networks: An Empirical Study—0
Learning to Find Translations and Transliterations on the Web—0
Learning to Generate Word Representations using Subword Information—0
Learning Topics and Positions from Debatepedia—0
Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models—0
Learning to recognise named entities in tweets by exploiting weakly labelled data—0
Learning to Search for Recognizing Named Entities in Twitter—0
Learning What's Easy: Fully Differentiable Neural Easy-First Taggers—0
Learning Word Embeddings for Low-Resource Languages by PU Learning—0
Learning Word Representations by Jointly Modeling Syntagmatic and Paradigmatic Relations—0
Leave no Place Behind: Improved Geolocation in Humanitarian Documents—0
Legal Entity Extraction using a Pointer Generator Network—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