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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/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/OnlpLab/Hebrew-Sentiment-Data
공식 코드AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With
https://github.com/OnlpLab/NEMO
공식 코드https://github.com/OnlpLab/NEMO-Corpus
공식 코드https://github.com/OntoGene/craft-st
공식 코드https://github.com/OpenCENIA/lightweight-spanish-language-models
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/Pruthwik/CRF-Based-Kannada-POS-Tagger
공식 코드Building a Kannada POS Tagger Using Machine Learning and Neural Network Models
https://github.com/Pruthwik/Training-POS-Chunk-Tagger-Using-BILSTM
공식 코드Building a Kannada POS Tagger Using Machine Learning and Neural Network Models
https://github.com/Ramprasad-Group/polymer_information_extraction
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/SUTDNLP/NNSegmentation
공식 코드Combining Discrete and Neural Features for Sequence Labeling
https://github.com/SaeedNajafi/ac-tagger
공식 코드https://github.com/SamySam0/SynDeidMLM
공식 코드https://github.com/SendongZhao/Multi-Task-Learning-for-MER-and-MEN
공식 코드https://github.com/Serega6678/NuNER
공식 코드NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data
https://github.com/ShannonAI/ChineseBert
공식 코드ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
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/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/Sreyan88/SemEval-2021-Toxic-Spans-Detection
공식 코드https://github.com/StanleySun233/HREB-CRF
공식 코드HREB-CRF: Hierarchical Reduced-bias EMA for Chinese Named Entity Recognition
https://github.com/StanleySun233/maritime-vts
공식 코드https://github.com/UKPLab/arxiv2018-bayesian-ensembles
공식 코드https://github.com/UKPLab/emnlp2017-bilstm-cnn-crf
공식 코드Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
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