https://github.com/GanjinZero/KeBioLM
공식 코드Improving Biomedical Pretrained Language Models with Knowledge
Papers with Code 아카이브
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/GanjinZero/Triaffine-nested-ner
공식 코드Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition
https://github.com/GeorgeLuImmortal/PUnifiedNER
공식 코드PUnifiedNER: A Prompting-based Unified NER System for Diverse Datasets
https://github.com/GeorgeLuImmortal/PaDeLLM_NER
공식 코드PaDeLLM-NER: Parallel Decoding in Large Language Models for Named Entity Recognition
https://github.com/HIT-ICES/AL-NER
공식 코드LTP: A New Active Learning Strategy for CRF-Based Named Entity Recognition
https://github.com/HIT-SCIR/ltp
공식 코드N-LTP: An Open-source Neural Language Technology Platform for Chinese
https://github.com/HSLCY/VCWE
공식 코드https://github.com/HornHehhf/PABI
공식 코드https://github.com/HornHehhf/TAWT
공식 코드https://github.com/INK-USC/RockNER
공식 코드https://github.com/INK-USC/TriggerNER
공식 코드TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition
https://github.com/IlyaGusev/summarus
공식 코드Advances of Transformer-Based Models for News Headline Generation
https://github.com/JinYuanLi0012/RiVEG
공식 코드LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition
https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/1.4.Biomedical_NER_SparkNLP_paper_reproduce.ipynb
공식 코드https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/tutorials/Certification_Trainings/Healthcare
공식 코드Improving Clinical Document Understanding on COVID-19 Research with Spark NLP
https://github.com/KGCP/MEL-TNNT
공식 코드https://github.com/KLUE-benchmark/KLUE
공식 코드https://github.com/Kungbib/swedish-bert-models
공식 코드Playing with Words at the National Library of Sweden -- Making a Swedish BERT
https://github.com/L-ENA/HealthINF2020
공식 코드Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering Tasks
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/LD-Net
공식 코드Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling
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/LorrinWWW/two-are-better-than-one
공식 코드Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders
https://github.com/MAGIC-AI4Med/RaTEScore
공식 코드https://github.com/Michael-Tanzer/BERT-mem-lowres
공식 코드Memorisation versus Generalisation in Pre-trained Language Models
https://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/NVIDIA/NeMo
공식 코드https://github.com/Nealcly/LAN
공식 코드Hierarchically-Refined Label Attention Network for Sequence Labeling
https://github.com/Nealcly/templateNER
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.