Named Entity Recognition Edit Task

Natural Language Processing

371

papers with code

benchmarks

datasets

About
Edit

Named entity recognition (NER) is the task of tagging entities in text with their corresponding type. 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 )

Benchmarks

Add a Result

DATASET	BEST METHOD	PAPER TITLE	COMPARE
CoNLL 2003 (English)	LUKE	LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention	See all
Ontonotes v5 (English)	BERT-MRC+DSC	Dice Loss for Data-imbalanced NLP Tasks	See all
ACE 2005	Ours: cross-sentence ALB	A Frustratingly Easy Approach for Entity and Relation Extraction	See all
GENIA	Biaffine-NER	Named Entity Recognition as Dependency Parsing	See all
BC5CDR	NER+PA+RL (PubMed)	Reinforcement-based denoising of distantly supervised NER with partial annotation	See all
NCBI-disease	BioBERT	BioBERT: a pre-trained biomedical language representation model for biomedical text mining	See all
SciERC	SpERT	Span-based Joint Entity and Relation Extraction with Transformer Pre-training	See all
ACE 2004	Ours: cross-sentence ALB	A Frustratingly Easy Approach for Entity and Relation Extraction	See all
Long-tail emerging entities	Flair embeddings	Contextual String Embeddings for Sequence Labeling	See all
JNLPBA	Spark NLP	Biomedical Named Entity Recognition at Scale	See all
CoNLL++	CrossWeigh + Pooled Flair	CrossWeigh: Training Named Entity Tagger from Imperfect Annotations	See all
CoNLL 2003 (German)	ACE + document-context	Automated Concatenation of Embeddings for Structured Prediction	See all
CoNLL 2002 (Spanish)	ACE + document-context	Automated Concatenation of Embeddings for Structured Prediction	See all
CoNLL 2002 (Dutch)	ACE + document-context	Automated Concatenation of Embeddings for Structured Prediction	See all
CoNLL 2003 (German) Revised	ACE + document-context	Automated Concatenation of Embeddings for Structured Prediction	See all
LINNAEUS	Spark NLP	Biomedical Named Entity Recognition at Scale	See all
WLPC	DyGIE	A General Framework for Information Extraction using Dynamic Span Graphs	See all
WetLab	BiLSTM-CRF with ELMo	Using Similarity Measures to Select Pretraining Data for NER	See all
Species-800	BioFLAIR	BioFLAIR: Pretrained Pooled Contextualized Embeddings for Biomedical Sequence Labeling Tasks	See all
Code-Switching English-Spanish NER	HME (word + BPE + char)	Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition	See all
ontontoes chinese v5	DGLSTM-CRF	Dependency-Guided LSTM-CRF for Named Entity Recognition	See all
CoNLL 2000	SWEM-CRF	Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms	See all
French Treebank	CamemBERT (subword masking)	CamemBERT: a Tasty French Language Model	See all
SoSciSoCi	Bi-LSTM-CRF (SSC->GSC)	Investigating Software Usage in the Social Sciences: A Knowledge Graph Approach	See all
LeNER-Br	LSTM-CRF	LeNER-Br: a Dataset for Named Entity Recognition in Brazilian Legal Text	See all
CoNLL 2003 NER dev	DeBERTalarge	DeBERTa: Decoding-enhanced BERT with Disentangled Attention	See all
CoNLL03	FLERT	FLERT: Document-Level Features for Named Entity Recognition	See all
BC5CDR-chemical	Trove	Ontology-driven weak supervision for clinical entity classification in electronic health records	See all
BC5CDR-disease	Trove	Ontology-driven weak supervision for clinical entity classification in electronic health records	See all
NCBI Disease	PubMedBERT uncased	Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing	See all
BC2GM	PubMedBERT uncased	Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing	See all

Libraries

AllenNLP

9,916 stars · 24 models

Subtasks

Chinese Named Entity Recognition

6 benchmarks

22 papers with code

Nested Named Entity Recognition

3 benchmarks

16 papers with code

Medical Named Entity Recognition

11 benchmarks

8 papers with code

Cross-Domain Named Entity Recognition

1 benchmark

5 papers with code

Named Entity Recognition In Vietnamese

1 benchmark

2 papers with code

See all 7 subtasks

Datasets

ACE 2005

GENIA

BC5CDR

SciERC

ACE 2004

See all 47 datasets

Greatest papers with code

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

NAACL 2019 • tensorflow/models •

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.

Ranked #1 on Question Answering on CoQA

COMMON SENSE REASONING CONVERSATIONAL RESPONSE SELECTION CROSS-LINGUAL NATURAL LANGUAGE INFERENCE NAMED ENTITY RECOGNITION NATURAL LANGUAGE UNDERSTANDING QUESTION ANSWERING SENTENCE CLASSIFICATION SENTIMENT ANALYSIS

69,488

Paper
Code

Semi-Supervised Sequence Modeling with Cross-View Training

EMNLP 2018 • tensorflow/models •

We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data.

Ranked #1 on CCG Supertagging on CCGBank

CCG SUPERTAGGING DEPENDENCY PARSING MACHINE TRANSLATION MULTI-TASK LEARNING NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING UNSUPERVISED REPRESENTATION LEARNING

69,480

Paper
Code

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

ICLR 2021 • huggingface/transformers •

Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.

Ranked #1 on Question Answering on SQuAD1.1 dev

COMMON SENSE REASONING COREFERENCE RESOLUTION LINGUISTIC ACCEPTABILITY NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE NATURAL LANGUAGE UNDERSTANDING QUESTION ANSWERING READING COMPREHENSION SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS WORD SENSE DISAMBIGUATION

43,811

Paper
Code

CamemBERT: a Tasty French Language Model

ACL 2020 • huggingface/transformers •

We show that the use of web crawled data is preferable to the use of Wikipedia data.

Ranked #1 on Dependency Parsing on Spoken Corpus

DEPENDENCY PARSING LANGUAGE MODELLING NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE PART-OF-SPEECH TAGGING

43,811

Paper
Code

FLERT: Document-Level Features for Named Entity Recognition

13 Nov 2020 • flairNLP/flair •

Current state-of-the-art approaches for named entity recognition (NER) using BERT-style transformers typically use one of two different approaches: (1) The first fine-tunes the transformer itself on the NER task and adds only a simple linear layer for word-level predictions.

Ranked #1 on Named Entity Recognition on CoNLL03

4 NAMED ENTITY RECOGNITION

10,214

Paper
Code

Deep contextualized word representations

NAACL 2018 • flairNLP/flair •

We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).

Ranked #2 on Citation Intent Classification on ACL-ARC (using extra training data)

CITATION INTENT CLASSIFICATION CONVERSATIONAL RESPONSE SELECTION COREFERENCE RESOLUTION LANGUAGE MODELLING NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC ROLE LABELING SENTIMENT ANALYSIS

10,214

Paper
Code

Semi-supervised sequence tagging with bidirectional language models

ACL 2017 • flairNLP/flair •

Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks.

Ranked #27 on Named Entity Recognition on CoNLL 2003 (English)

CHUNKING NAMED ENTITY RECOGNITION

10,214

Paper
Code

Neural Architectures for Named Entity Recognition

NAACL 2016 • flairNLP/flair •

State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available.

Ranked #6 on Named Entity Recognition on CoNLL++

NAMED ENTITY RECOGNITION

10,214

Paper
Code

Named Entity Recognition with Bidirectional LSTM-CNNs

TACL 2016 • flairNLP/flair •

Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance.

Ranked #17 on Named Entity Recognition on Ontonotes v5 (English)

ENTITY LINKING FEATURE ENGINEERING NAMED ENTITY RECOGNITION WORD EMBEDDINGS

10,214

Paper
Code