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
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.
Re-Examine Distantly Supervised NER: A New Benchmark and a Simple Approach
liyp0095/CuPUL
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This paper delves into Named Entity Recognition (NER) under the framework of Distant Supervision (DS-NER), where the main challenge lies in the compromised quality of labels due to inherent errors such as false positives, false negatives, and positive type errors.
Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction
We then fine-tuned the spaCy NER tool and validated that having a dataset tailor-made for Malaysian English could improve the performance of NER in Malaysian English significantly.
A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction
It breaks the generation into a two-step pipeline: initially, LLMs generate answers in natural language as intermediate responses.
PaDeLLM-NER: Parallel Decoding in Large Language Models for Named Entity Recognition
GeorgeLuImmortal/PaDeLLM_NER
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In this study, we aim to reduce generation latency for Named Entity Recognition (NER) with Large Language Models (LLMs).
A Survey of Large Language Models in Finance (FinLLMs)
This survey provides a comprehensive overview of FinLLMs, including their history, techniques, performance, and opportunities and challenges.
Different Tastes of Entities: Investigating Human Label Variation in Named Entity Annotations
Named Entity Recognition (NER) is a key information extraction task with a long-standing tradition.
Gazetteer-Enhanced Bangla Named Entity Recognition with BanglaBERT Semantic Embeddings K-Means-Infused CRF Model
In this research, we explored the existing state of research in Bangla Named Entity Recognition.
ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks
boleima/topro
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However, most previous studies primarily focused on sentence-level classification tasks, and only a few considered token-level labeling tasks such as Named Entity Recognition (NER) and Part-of-Speech (POS) tagging.
