Papers with Code 아카이브
Named Entity Recognition (NER)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 76
- 보존된 평가 기록
- 439
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/LeeSureman/Batch_Parallel_LatticeLSTM
https://github.com/LeoWood/bert-horovod
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Linar23/Research_work
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/LorenzoAgnolucci/BERT_for_ABSA
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/LorrinWWW/Pyramid
https://github.com/LoveYang/bert_test
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/LucDomingo/pampo_plus
PAMPO: using pattern matching and pos-tagging for effective Named Entities recognition in Portuguese
https://github.com/MINGG2333/bert_ms
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MLH-Fellowship/Social-BERTerfly
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MOHAMEDELDAKDOUKY/bert_adjusted
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MS-Mind/MS-Code-01/tree/main/soft_masked_bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MS-P3/code5/tree/main/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MaZhiyuanBUAA/bert-tf1.4.0
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MalteHB/-l-ctra
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ManasRMohanty/DS5500-capstone
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MansMeg/IntroML
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MarcosGrzeca/drunktweets
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MatthewSwahn/news-headlines-sarcasm-detector
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Maz101/Bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Maz101/pretrainingdataset
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/MeRajat/SolvingAlmostAnythingWithBert
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/MichaelZhouwang/LMlexsub
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Microsoft/AzureML-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Mind23-2/MindCode-100
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/Mind23-2/MindCode-133
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Mind23-2/MindCode-135
https://github.com/Mind23-2/MindCode-144
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/Mind23-2/MindCode-145
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
https://github.com/Mind23-2/MindCode-161
https://github.com/Mind23-2/MindCode-98
https://github.com/Mind23-2/MindCode-99
ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/MindCode-4/code-11/tree/main/dice-loss-for-data-imbalanced-nlp-tasks
https://github.com/MindCode-4/code-3/tree/main/deberta
https://github.com/MindCode-4/code-3/tree/main/deberta_v2
https://github.com/MindCode-4/code-3/tree/main/ernie
ERNIE: Enhanced Representation through Knowledge Integration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.