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Named Entity Recognition (NER)

672 평가 기록 114 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

<span style="color:grey; opacity: 0.6">( Image credit: Zalando )</span>

Named Entity Recognition (NER)

논문

On the Effectiveness of Compact Biomedical Transformers

Biomedical Named Entity Recognition at Scale

SciBERT: A Pretrained Language Model for Scientific Text

A Frustratingly Easy Approach for Entity and Relation Extraction

A Neural Named Entity Recognition and Multi-Type Normalization Tool for Biomedical Text Mining

A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition

BERN2: an advanced neural biomedical named entity recognition and normalization tool

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

BioMegatron: Larger Biomedical Domain Language Model

CamemBERT: a Tasty French Language Model

Comparing and combining some popular NER approaches on Biomedical tasks

Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

ELECTRAMed: a new pre-trained language representation model for biomedical NLP

Evaluation of large language model performance on the Biomedical Language Understanding and Reasoning Benchmark

Focusing on Potential Named Entities During Active Label Acquisition

GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction

Improving Biomedical Named Entity Recognition with Syntactic Information

Improving Biomedical Pretrained Language Models with Knowledge

Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning

Learning A Unified Named Entity Tagger From Multiple Partially Annotated Corpora For Efficient Adaptation

LinkBERT: Pretraining Language Models with Document Links

NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data

Parallel Instance Query Network for Named Entity Recognition

SciFive: a text-to-text transformer model for biomedical literature

Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark