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)
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Libraries
Use these libraries to find Named Entity Recognition (NER) models and implementations
BC5CDR
PubMedQA
FUNSD
SciERC
GENIA
Subtasks
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NER
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Nested Named Entity Recognition
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Chinese Named Entity Recognition
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Few-shot NER
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Few-shot NER
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Medical Named Entity Recognition
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Multilingual Named Entity Recognition
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Cross-Domain Named Entity Recognition
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Named Entity Recognition In Vietnamese
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Multi-modal Named Entity Recognition
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Zero-shot Named Entity Recognition (NER)
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Toponym Recognition
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Scientific Concept Extraction
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Multi-Grained Named Entity Recognition
Latest papers with no code
Do "English" Named Entity Recognizers Work Well on Global Englishes?
We test widely used NER toolkits and transformer models, including models using the pre-trained contextual models RoBERTa and ELECTRA, on three datasets: a commonly used British English newswire dataset, CoNLL 2003, a more American focused dataset OntoNotes, and our global dataset.
Few-shot Name Entity Recognition on StackOverflow
StackOverflow, with its vast question repository and limited labeled examples, raise an annotation challenge for us.
ToNER: Type-oriented Named Entity Recognition with Generative Language Model
In recent years, the fine-tuned generative models have been proven more powerful than the previous tagging-based or span-based models on named entity recognition (NER) task.
LLMs in Biomedicine: A study on clinical Named Entity Recognition
Large Language Models (LLMs) demonstrate remarkable versatility in various NLP tasks but encounter distinct challenges in biomedicine due to medical language complexities and data scarcity.
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding
Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa.
Enhancing Software-Related Information Extraction via Single-Choice Question Answering with Large Language Models
This paper describes our participation in the Shared Task on Software Mentions Disambiguation (SOMD), with a focus on improving relation extraction in scholarly texts through generative Large Language Models (LLMs) using single-choice question-answering.
How much reliable is ChatGPT's prediction on Information Extraction under Input Perturbations?
In this paper, we assess the robustness (reliability) of ChatGPT under input perturbations for one of the most fundamental tasks of Information Extraction (IE) i. e. Named Entity Recognition (NER).
SCANNER: Knowledge-Enhanced Approach for Robust Multi-modal Named Entity Recognition of Unseen Entities
Our approach demonstrates competitive performance on the NER benchmark and surpasses existing methods on both MNER and GMNER benchmarks.
Utilizing AI and Social Media Analytics to Discover Adverse Side Effects of GLP-1 Receptor Agonists
Adverse side effects (ASEs) of drugs, revealed after FDA approval, pose a threat to patient safety.
Augmenting NER Datasets with LLMs: Towards Automated and Refined Annotation
In the field of Natural Language Processing (NLP), Named Entity Recognition (NER) is recognized as a critical technology, employed across a wide array of applications.
