https://bitbucket.org/gg42554/cl-sts
공식 코드A Resource-Light Method for Cross-Lingual Semantic Textual Similarity
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/131250208/infextraction
공식 코드Discontinuous Named Entity Recognition as Maximal Clique Discovery
https://github.com/193746/jsrsl
공식 코드Joint Automatic Speech Recognition And Structure Learning For Better Speech Understanding
https://github.com/4ai/ls-llama
공식 코드https://github.com/4ldk/SubRegWeigh
공식 코드SubRegWeigh: Effective and Efficient Annotation Weighing with Subword Regularization
https://github.com/Adaxry/GCDT
공식 코드GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling
https://github.com/Aditi138/Embeddings
공식 코드https://github.com/Aditi138/EntityTargetedActiveLearning
공식 코드https://github.com/AidenHuen/FGN-NER
공식 코드FGN: Fusion Glyph Network for Chinese Named Entity Recognition
https://github.com/Alex-Lyu0419/Financial-Named-Entity-Recognition-How-Far-Can-LLM-Go
공식 코드https://github.com/Alibaba-NLP/ACE
공식 코드Automated Concatenation of Embeddings for Structured Prediction
https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus
공식 코드KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi
https://github.com/Andy-jqa/bioelmo
공식 코드https://github.com/BIDS-Xu-Lab/Kiwi-LLaMA
공식 코드Information Extraction from Clinical Notes: Are We Ready to Switch to Large Language Models?
https://github.com/BananaTonic/Material_Synthesis_Corpus
공식 코드Analyzing Research Trends in Inorganic Materials Literature Using NLP
https://github.com/BordiaS/LegalLens_inference
공식 코드https://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/CogComp/PABI
공식 코드https://github.com/CogComp/QuASE
공식 코드https://github.com/CogComp/zoe
공식 코드https://github.com/CompNet/conivel
공식 코드The Role of Global and Local Context in Named Entity Recognition
https://github.com/CompNet/nerwip
공식 코드A Comparison of Named Entity Recognition Tools Applied to Biographical Texts
https://github.com/CongSun-dlut/BioBERT-MRC
공식 코드Biomedical named entity recognition using BERT in the machine reading comprehension framework
https://github.com/DMIRLAB-Group/SSD
공식 코드Semi-Supervised Disentangled Framework for Transferable Named Entity Recognition
https://github.com/DS3Lab/ner-at-first-sight
공식 코드Entity Recognition at First Sight: Improving NER with Eye Movement Information
https://github.com/DS3Lab/zuco-nlp
공식 코드https://github.com/DevSinghSachan/syntax-augmented-bert
공식 코드Do Syntax Trees Help Pre-trained Transformers Extract Information?
https://github.com/DigitalDigger/MeTooMaastrichtChatbot
공식 코드#MeTooMaastricht: Building a chatbot to assist survivors of sexual harassment
https://github.com/EagleW/Chem-FINESE
공식 코드Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction
https://github.com/Emma1066/Self-Improve-Zero-Shot-NER
공식 코드Self-Improving for Zero-Shot Named Entity Recognition with Large Language Models
https://github.com/ExpResults/segrep-for-nn-semicrf
공식 코드Exploring Segment Representations for Neural Segmentation Models
https://github.com/FranxYao/Partially-Observed-TreeCRFs
공식 코드Nested Named Entity Recognition with Partially-Observed TreeCRFs
https://github.com/Fritz449/ProtoNER
공식 코드https://github.com/GT-SALT/LADA
공식 코드Local Additivity Based Data Augmentation for Semi-supervised NER
https://github.com/GanjinZero/BioBART
공식 코드BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.