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Named Entity Recognition (NER)
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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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/explosion/projects/tree/v3/benchmarks/ner_embeddings
공식 코드https://github.com/eyalbd2/scratchda
공식 코드https://github.com/facebookresearch/Clinical-Trial-Parser
공식 코드Information Extraction of Clinical Trial Eligibility Criteria
https://github.com/facebookresearch/quip
공식 코드Question Answering Infused Pre-training of General-Purpose Contextualized Representations
https://github.com/fastnlp/TENER
공식 코드TENER: Adapting Transformer Encoder for Named Entity Recognition
https://github.com/fastnlp/fastHan
공식 코드https://github.com/fdschmidt93/free-lunch-xlt
공식 코드Free Lunch: Robust Cross-Lingual Transfer via Model Checkpoint Averaging
https://github.com/fdschmidt93/ofa-xlt
공식 코드https://github.com/fe1ixxu/mixed-gradient-few-shot
공식 코드https://github.com/fishjh2/merge_label
공식 코드Merge and Label: A novel neural network architecture for nested NER
https://github.com/fitz-like-coding/cwtm
공식 코드CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling
https://github.com/flairNLP/flair
공식 코드HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition
https://github.com/flairnlp/cleanconll
공식 코드CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset
https://github.com/flxst/nerblackbox
공식 코드Adaptive Fine-Tuning of Transformer-Based Language Models for Named Entity Recognition
https://github.com/flyingmothman/bionlp
공식 코드Comparing and combining some popular NER approaches on Biomedical tasks
https://github.com/foxlf823/sodner
공식 코드A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition
https://github.com/frankaging/Causal-Distill
공식 코드https://github.com/frankkramer-lab/GERNERMED
공식 코드https://github.com/frankkramer-lab/gernermed-pp
공식 코드https://github.com/frankkramer-lab/gptnermed
공식 코드Annotated Dataset Creation through General Purpose Language Models for non-English Medical NLP
https://github.com/fraunhofer-iais/arabic_nlp
공식 코드https://github.com/fulstock/Methods-for-Recognizing-Nested-Terms
공식 코드https://github.com/fuzihaofzh/band
공식 코드https://github.com/gabriel819/zeroshot_ner
공식 코드Zero-Shot Cross-Lingual NER Using Phonemic Representations for Low-Resource Languages
https://github.com/ganchengguang/SLG-framework
공식 코드https://github.com/gchhablani/toxic-spans-detection
공식 코드https://github.com/gedeonmate/hungarian_static_embeddings
공식 코드A Comparative Analysis of Static Word Embeddings for Hungarian
https://github.com/gentaiscool/meta-emb
공식 코드Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition
https://github.com/geoai-lab/EUPEG
공식 코드https://github.com/geode-project/ner-llm
공식 코드Evaluating Named Entity Recognition Using Few-Shot Prompting with Large Language Models
https://github.com/gingasan/adversarialsa
공식 코드https://github.com/glample/tagger
공식 코드https://github.com/globus-labs/covid-nlp
공식 코드https://github.com/gmichalo/UmlsBERT
공식 코드https://github.com/gmpoli/electramed
공식 코드ELECTRAMed: a new pre-trained language representation model for biomedical NLP
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