Named Entity Recognition (NER)
891 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
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.
Extracting Biomedical Entities from Noisy Audio Transcripts
Our dataset offers a comprehensive collection of almost 2, 000 clean and noisy recordings.
Korean Bio-Medical Corpus (KBMC) for Medical Named Entity Recognition
Named Entity Recognition (NER) plays a pivotal role in medical Natural Language Processing (NLP).
MRC-based Nested Medical NER with Co-prediction and Adaptive Pre-training
In medical information extraction, medical Named Entity Recognition (NER) is indispensable, playing a crucial role in developing medical knowledge graphs, enhancing medical question-answering systems, and analyzing electronic medical records.
CHisIEC: An Information Extraction Corpus for Ancient Chinese History
Additionally, we have evaluated the capabilities of Large Language Models (LLMs) in the context of tasks related to ancient Chinese history.
