https://github.com/Kungbib/swedish-bert-models
공식 코드Playing with Words at the National Library of Sweden -- Making a Swedish BERT
Papers with Code 아카이브
NER
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The named entity recognition (NER) involves identification of key information in the text and classification into a set of predefined categories. This includes standard entities in the text like Part of Speech (PoS) and entities like places, names etc...
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/LeePleased/NegSampling-NER
공식 코드Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition
https://github.com/LeeSureman/Flat-Lattice-Transformer
공식 코드https://github.com/LemXiong/CPLL
공식 코드A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition
https://github.com/LindgeW/MetaAug4NER
공식 코드Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting
https://github.com/LiyuanLucasLiu/LM-LSTM-CRF
공식 코드Empower Sequence Labeling with Task-Aware Neural Language Model
https://github.com/LiyuanLucasLiu/Raw-to-End
공식 코드https://github.com/MAGIC-AI4Med/RaTEScore
공식 코드https://github.com/Microsoft/Recognizers-Text
공식 코드https://github.com/Multimodal-NER/RpBERT
공식 코드RpBERT: A Text-image Relation Propagation-based BERT Model for Multimodal NER
https://github.com/NEISSproject/NERDatasets
공식 코드https://github.com/NEISSproject/tf2_neiss_nlp
공식 코드https://github.com/Nealcly/LAN
공식 코드Hierarchically-Refined Label Attention Network for Sequence Labeling
https://github.com/NeuralSentinel/DistALANER
공식 코드https://github.com/Ninarehm/NERGenderBias
공식 코드Man is to Person as Woman is to Location: Measuring Gender Bias in Named Entity Recognition
https://github.com/NorskRegnesentral/skweak
공식 코드https://github.com/NorskRegnesentral/weak-supervision-for-NER
공식 코드Named Entity Recognition without Labelled Data: A Weak Supervision Approach
https://github.com/OntoGene/craft-st
공식 코드https://github.com/OpenCENIA/lightweight-spanish-language-models
공식 코드https://github.com/RiTUAL-UH/cs_elmo
공식 코드From English to Code-Switching: Transfer Learning with Strong Morphological Clues
https://github.com/RiTUAL-UH/multimodal_NER
공식 코드Can images help recognize entities? A study of the role of images for Multimodal NER
https://github.com/RiTUAL-UH/trending_NER
공식 코드Mitigating Temporal-Drift: A Simple Approach to Keep NER Models Crisp
https://github.com/Rosenberg37/EntityDetection
공식 코드https://github.com/Rosenberg37/GraphNER
공식 코드Type-supervised sequence labeling based on the heterogeneous star graph for named entity recognition
https://github.com/SUSTechBruce/G-MAP
공식 코드G-MAP: General Memory-Augmented Pre-trained Language Model for Domain Tasks
https://github.com/SaeedNajafi/ac-tagger
공식 코드https://github.com/SamySam0/SynDeidMLM
공식 코드https://github.com/Serega6678/NuNER
공식 코드NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data
https://github.com/ShannonAI/glyce
공식 코드https://github.com/SinaLab/ArabicNER
공식 코드Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT
https://github.com/Sreyan88/BioAug
공식 코드BioAug: Conditional Generation based Data Augmentation for Low-Resource Biomedical NER
https://github.com/StanleySun233/maritime-vts
공식 코드https://github.com/UKPLab/emnlp2017-bilstm-cnn-crf
공식 코드https://github.com/VinAIResearch/PhoNER_COVID19
공식 코드https://github.com/VinAIResearch/PhoNLP
공식 코드https://github.com/WHUIR/ME-CNER
공식 코드Exploiting Multiple Embeddings for Chinese Named Entity Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.