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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/few-shot-NER-benchmark/BaselineCode
https://github.com/gaohuan2015/NLPTool
Semi-supervised sequence tagging with bidirectional language models
https://github.com/h2oai/h2ogpt
https://github.com/hipe-eval/hipe-2022-data
Named Entity Recognition and Classification on Historical Documents: A Survey
https://github.com/hu-ner/hunflair-experiments
HunFlair: An Easy-to-Use Tool for State-of-the-Art Biomedical Named Entity Recognition
https://github.com/huspacy/huspacy
HuSpaCy: an industrial-strength Hungarian natural language processing toolkit
https://github.com/issam9/finetuning-bert-models-for-arabic-dialect-detection
AraBERT: Transformer-based Model for Arabic Language Understanding
https://github.com/jackboyla/glirel
GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer
https://github.com/jbogensperger/DRUG_CROSSNER
https://github.com/jiesutd/PyTorchSeqLabel
Design Challenges and Misconceptions in Neural Sequence Labeling
https://github.com/jjfiv/searchie-extraction
https://github.com/john-hewitt/conditional-probing
Fast and Accurate Entity Recognition with Iterated Dilated Convolutions
https://github.com/kingcong/models/tree/main/bert
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
https://github.com/klimzaporojets/consistent-el
DWIE: an entity-centric dataset for multi-task document-level information extraction
https://github.com/lfoppiano/supercon
Automatic extraction of materials and properties from superconductors scientific literature
https://github.com/ljy19970415/unibrain
https://github.com/luozhilin99/bert
NEZHA: Neural Contextualized Representation for Chinese Language Understanding
https://github.com/mariananeves/annotation-tools
https://github.com/michael-beukman/nertransfer
https://github.com/microsoft/CyBERTron-LM
DiffusionNER: Boundary Diffusion for Named Entity Recognition
https://github.com/minhpqn/vietner
https://github.com/minstar/PMI
https://github.com/mramly/CairoMent
AraBERT: Transformer-based Model for Arabic Language Understanding
https://github.com/msfasha/Arabic-NLP
AraBERT: Transformer-based Model for Arabic Language Understanding
https://github.com/ncbi-nlp/BLUE_Benchmark
CollaboNet: collaboration of deep neural networks for biomedical named entity recognition
https://github.com/nlp-uoregon/famie
https://github.com/nyu-mll/jiant/tree/master/probing
https://github.com/oya163/nepali-ner
https://github.com/phnk/D7047E
Word2Vec: Optimal Hyper-Parameters and Their Impact on NLP Downstream Tasks
https://github.com/phsfr/UD_Persian-PerDT
https://github.com/pth1993/NNVLP
https://github.com/ragerri/basque-embeddings-lrec2020
Give your Text Representation Models some Love: the Case for Basque
https://github.com/raghavjajodia/unsupervised_nlu
Knowledge-Augmented Language Model and its Application to Unsupervised Named-Entity Recognition
https://github.com/revdotcom/fstalign
https://github.com/rishabgit/genomic-info-from-papers
Single versus Multiple Annotation for Named Entity Recognition of Mutations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.