https://github.com/zenanz/ChemPatentEmbeddings
공식 코드Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings
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/zenhjunpro/atsen
공식 코드https://github.com/zerohd4869/SLK-NER
공식 코드SLK-NER: Exploiting Second-order Lexicon Knowledge for Chinese NER
https://github.com/zhang-mozhi/promptner
공식 코드PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search
https://github.com/zhangsha1024/HVDistill
공식 코드https://github.com/zhaoxy92/GLaRA
공식 코드GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition
https://github.com/zhenxuan-zhang/gema_score
공식 코드GEMA-Score: Granular Explainable Multi-Agent Score for Radiology Report Evaluation
https://github.com/zhhvvv/cner-uav
공식 코드https://github.com/zhzhengit/LinkNER
공식 코드LinkNER: Linking Local Named Entity Recognition Models to Large Language Models using Uncertainty
https://github.com/zjunlp/DeepKE/tree/main/example/ner/cross
공식 코드One Model for All Domains: Collaborative Domain-Prefix Tuning for Cross-Domain NER
https://github.com/zjunlp/DeepKE/tree/main/example/ner/few-shot
공식 코드LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting
https://github.com/zjunlp/MKGformer
공식 코드Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion
https://github.com/zjunlp/deepke
공식 코드One Model for All Domains: Collaborative Domain-Prefix Tuning for Cross-Domain NER
https://github.com/zl-liu/radiation-oncology-nlp-database
공식 코드https://github.com/zliucr/CrossNER
공식 코드https://github.com/zmd971202/OpenNER
공식 코드Neural Correction Model for Open-Domain Named Entity Recognition
https://github.com/zqtan1024/sequence-to-set
공식 코드A Sequence-to-Set Network for Nested Named Entity Recognition
https://github.com/zwkatgithub/DSCAU
공식 코드De-biasing Distantly Supervised Named Entity Recognition via Causal Intervention
https://github.com/zwkatgithub/HGL
공식 코드Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model
https://github.com/zzz47zzz/CFNER
공식 코드Distilling Causal Effect from Miscellaneous Other-Class for Continual Named Entity Recognition
https://worksheets.codalab.org/worksheets/0x2ae89944846444539c2d08a0b7ff3f6f
공식 코드https://github.com/2023-MindSpore-1/ms-code-11
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
https://github.com/2023-MindSpore-1/ms-code-12
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
https://github.com/2023-MindSpore-1/ms-code-160
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
https://github.com/2023-MindSpore-1/ms-code-56
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
https://github.com/2023-MindSpore-4/Code16/tree/main/guitao/MINER-main
https://github.com/4ai/bellm
https://github.com/Aitslab/BioNLP
Interpretable Multi-dataset Evaluation for Named Entity Recognition
https://github.com/BITCS-Information-Retrieval-2020/search-rattailcollagen1
Let's Stop Incorrect Comparisons in End-to-end Relation Extraction!
https://github.com/CogStack/MedCAT
Foresight -- Generative Pretrained Transformer (GPT) for Modelling of Patient Timelines using EHRs
https://github.com/Coopercoppers/PFN
Let's Stop Incorrect Comparisons in End-to-end Relation Extraction!
https://github.com/Eeun-ju/NLP-study
https://github.com/Franck-Dernoncourt/NeuroNER
NeuroNER: an easy-to-use program for named-entity recognition based on neural networks
https://github.com/Houlong66/lattice_lstm_with_pytorch
https://github.com/IreneMorazzoni/def_2_vec_irene
https://github.com/LIAAD/py-pampo
PAMPO: using pattern matching and pos-tagging for effective Named Entities recognition in Portuguese
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.