https://github.com/AidenHuen/FGN-NER
공식 코드FGN: Fusion Glyph Network for Chinese Named Entity Recognition
Papers with Code 아카이브
Chinese Named Entity Recognition
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Chinese named entity recognition is a subtask of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. from Chinese text (Source: Adapted from Wikipedia).
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/CLUEbenchmark/CLUENER2020
공식 코드CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese
https://github.com/CoderMusou/MECT4CNER
공식 코드MECT: Multi-Metadata Embedding based Cross-Transformer for Chinese Named Entity Recognition
https://github.com/LeeSureman/Flat-Lattice-Transformer
공식 코드https://github.com/PaddlePaddle/ERNIE
공식 코드ERNIE 2.0: A Continual Pre-training Framework for Language Understanding
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-1.0
공식 코드ERNIE: Enhanced Representation through Knowledge Integration
https://github.com/ShannonAI/dice_loss_for_NLP
공식 코드https://github.com/ShannonAI/glyce
공식 코드https://github.com/ShannonAI/mrc-for-flat-nested-ner
공식 코드https://github.com/StanleySun233/HREB-CRF
공식 코드HREB-CRF: Hierarchical Reduced-bias EMA for Chinese Named Entity Recognition
https://github.com/WHUIR/ME-CNER
공식 코드Exploiting Multiple Embeddings for Chinese Named Entity Recognition
https://github.com/allanj/ner_with_dependency
공식 코드https://github.com/codermusou/nflat4cner
공식 코드NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition
https://github.com/conglei2xu/global-context-mechanism
공식 코드Supplementary Features of BiLSTM for Enhanced Sequence Labeling
https://github.com/cuhksz-nlp/AESINER
공식 코드Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information
https://github.com/cuhksz-nlp/SANER
공식 코드Named Entity Recognition for Social Media Texts with Semantic Augmentation
https://github.com/daiyongya/markbert
공식 코드https://github.com/dugu9sword/LEMON
공식 코드Chinese Named Entity Recognition Augmented with Lexicon Memory
https://github.com/fastnlp/TENER
공식 코드TENER: Adapting Transformer Encoder for Named Entity Recognition
https://github.com/hkust-lib-ds/P001-PUBLIC_Chinese-NER-Tool
공식 코드https://github.com/jiesutd/LatticeLSTM
공식 코드https://github.com/knowledgeresearch/kaner
공식 코드Rethinking the Value of Gazetteer in Chinese Named Entity Recognition
https://github.com/ljynlp/w2ner
공식 코드Unified Named Entity Recognition as Word-Word Relation Classification
https://github.com/microsoft/vert-papers/tree/master/papers/CAN-NER
공식 코드CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition
https://github.com/modelscope/AdaSeq/tree/master/examples/RaNER
공식 코드Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
https://github.com/modelscope/AdaSeq/tree/master/examples/babert
공식 코드Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling
https://github.com/modelscope/adaseq
공식 코드Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
https://github.com/phenixace/MFE-NER
공식 코드MFE-NER: Multi-feature Fusion Embedding for Chinese Named Entity Recognition
https://github.com/sinovation/ZEN
공식 코드ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations
https://github.com/syuoni/eznlp
공식 코드https://github.com/tricktreat/diffusionner
공식 코드DiffusionNER: Boundary Diffusion for Named Entity Recognition
https://github.com/tricktreat/locate-and-label
공식 코드Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition
https://github.com/tricktreat/piqn
공식 코드Parallel Instance Query Network for Named Entity Recognition
https://github.com/v-mipeng/LexiconAugmentedNER
공식 코드https://github.com/zerohd4869/SLK-NER
공식 코드SLK-NER: Exploiting Second-order Lexicon Knowledge for Chinese NER
https://github.com/2023-MindSpore-1/ms-code-5/tree/main/emotect
ERNIE: Enhanced Representation through Knowledge Integration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.