https://github.com/Michael-Tanzer/BERT-mem-lowres
공식 코드Memorisation versus Generalisation in Pre-trained Language Models
Papers with Code 아카이브
Low Resource Named Entity Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Low resource named entity recognition is the task of using data and models available for one language for which ample such resources are available (e.g., English) to solve named entity recognition tasks in another, commonly more low-resource, language.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/afshinrahimi/mmner
공식 코드https://github.com/dfki-nlp/fewie
공식 코드A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition
https://github.com/edchengg/gollie-transfusion
공식 코드Translation and Fusion Improves Zero-shot Cross-lingual Information Extraction
https://github.com/hitz-zentroa/gollie
공식 코드GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction
https://github.com/houking-can/RDANER
공식 코드A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition
https://github.com/neulab/soft-gazetteers
공식 코드https://github.com/ntunlp/Zero-Shot-Cross-Lingual-NER
공식 코드https://github.com/stefan-it/historic-ner
공식 코드https://github.com/uds-lsv/anea
공식 코드ANEA: Distant Supervision for Low-Resource Named Entity Recognition
https://github.com/uds-lsv/noise-matrix-ner
공식 코드Feature-Dependent Confusion Matrices for Low-Resource NER Labeling with Noisy Labels
https://github.com/unveiled-the-red-hat/SEE-Few
공식 코드SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition
https://github.com/wjx-git/deptriggerner
공식 코드Low-Resource Named Entity Recognition Based on Multi-hop Dependency Trigger
https://github.com/RussianNLP/TAPE
Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation
https://github.com/dbmdz/historic-ner
https://github.com/ovbystrova/InstructionNER
InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.