Named Entity Recognition (NER)
886 papers with code • 76 benchmarks • 122 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Named Entity Recognition (NER)
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Named Entity Recognition (NER) models and implementations
BC5CDR
PubMedQA
FUNSD
SciERC
GENIA
Subtasks
-
NER
-
Nested Named Entity Recognition
-
Chinese Named Entity Recognition
-
Few-shot NER
-
Few-shot NER
-
Medical Named Entity Recognition
-
Multilingual Named Entity Recognition
-
Cross-Domain Named Entity Recognition
-
Named Entity Recognition In Vietnamese
-
Multi-modal Named Entity Recognition
-
Zero-shot Named Entity Recognition (NER)
-
Toponym Recognition
-
Scientific Concept Extraction
-
Multi-Grained Named Entity Recognition
Latest papers
REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE).
Intent Detection and Entity Extraction from BioMedical Literature
Biomedical queries have become increasingly prevalent in web searches, reflecting the growing interest in accessing biomedical literature.
Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets
csu-signal/xlingual-robustness
•
•
Multilingual Language Models (MLLMs) exhibit robust cross-lingual transfer capabilities, or the ability to leverage information acquired in a source language and apply it to a target language.
ELLEN: Extremely Lightly Supervised Learning For Efficient Named Entity Recognition
In a zero-shot setting, ELLEN also achieves over 75% of the performance of a strong, fully supervised model trained on gold data.
Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data
Named Entity Recognition (NER) is a fundamental task to extract key information from texts, but annotated resources are scarce for dialects.
Evaluating Named Entity Recognition: Comparative Analysis of Mono- and Multilingual Transformer Models on Brazilian Corporate Earnings Call Transcriptions
By curating a comprehensive dataset comprising 384 transcriptions and leveraging weak supervision techniques for annotation, we evaluate the performance of monolingual models trained on Portuguese (BERTimbau and PTT5) and multilingual models (mBERT and mT5).
ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models
Although Large Language Models (LLMs) exhibit remarkable adaptability across domains, these models often fall short in structured knowledge extraction tasks such as named entity recognition (NER).
Rethinking Negative Instances for Generative Named Entity Recognition
yyding1/gner
•
•
In the Named Entity Recognition (NER) task, recent advancements have seen the remarkable improvement of LLMs in a broad range of entity domains via instruction tuning, by adopting entity-centric schema.
DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software Ecosystem
With the AI revolution in place, the trend for building automated systems to support professionals in different domains such as the open source software systems, healthcare systems, banking systems, transportation systems and many others have become increasingly prominent.
NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data
Large Language Models (LLMs) have shown impressive abilities in data annotation, opening the way for new approaches to solve classic NLP problems.
