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
Fast and Accurate Decision Trees for Natural Language Processing Tasks—0
CAp 2017 challenge: Twitter Named Entity Recognition—0
Feature-Rich Twitter Named Entity Recognition and Classification—0
Federated Named Entity Recognition—0
Application of Pre-training Models in 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
Can Data Diversity Enhance Learning Generalization?—0
Facebook 活動事件擷取系統(Facebook Activity Event Extraction System)[In Chinese]—0
CCS Explorer: Relevance Prediction, Extractive Summarization, and Named Entity Recognition from Clinical Cohort Studies—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
FaBERT: Pre-training BERT on Persian Blogs—0
AI-KU at SemEval-2016 Task 11: Word Embeddings and Substring Features for Complex Word Identification—0
Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks—0
Few-shot learning for medical text: A systematic review—0
Adapting the TTL Romanian POS Tagger to the Biomedical Domain—0
Biomedical Mention Disambiguation using a Deep Learning Approach—0
Generating Polarity Lexicons with WordNet propagation in 5 languages—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
F10-SGD: Fast Training of Elastic-net Linear Models for Text Classification and Named-entity Recognition—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
Extrinsic Factors Affecting the Accuracy of Biomedical NER—0
Can BERT Dig It? -- Named Entity Recognition for Information Retrieval in the Archaeology Domain—0
Fighting Against the Repetitive Training and Sample Dependency Problem in Few-shot Named Entity Recognition—0
Character-Level Feature Extraction with Densely Connected Networks—0
Extreme Multi-Label Classification with Label Masking for Product Attribute Value Extraction—0
Extract-Select: A Span Selection Framework for Nested Named Entity Recognition with Generative Adversarial Training—0
Application of Data Encryption in Chinese Named Entity Recognition—0
A Biomedical Pipeline to Detect Clinical and Non-Clinical Named Entities—0
Extraction of Gene-Environment Interaction from the Biomedical Literature—0
A Hybrid Stepwise Approach for De-identifying Person Names in Clinical Documents—0
Extracting Spatial Entities and Relations in Korean Text—0
Cheap and easy entity evaluation—0
Extracting Semantics from Maintenance Records—0
Fine-grained Location Extraction via Curriculum Learning—0
CalibreNet: Calibration Networks for Multilingual Sequence Labeling—0
Finely Tuned, 2 Billion Token Based Word Embeddings for Portuguese—0
Generate it. A simple method for End-to-End Relation Extraction—0
Chemical detection and indexing in PubMed full text articles using deep learning and rule-based methods—0
Fine-tuning for Named Entity Recognition Using Part-of-Speech Tagging—0
Fine Tuning Named Entity Extraction Models for the Fantasy Domain—0
Generating Abbreviations for Chinese Named Entities Using Recurrent Neural Network with Dynamic Dictionary—0
Extracting Relations between Non-Standard Entities using Distant Supervision and Imitation Learning—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