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Open Information Extraction
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In natural language processing, open information extraction is the task of generating a structured, machine-readable representation of the information in text, usually in the form of triples or n-ary propositions (Source: Wikipedia).
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sagorbrur/bnlp
공식 코드BNLP: Natural language processing toolkit for Bengali language
https://github.com/salokr/email-event-extraction
공식 코드https://github.com/samanjoy2/gazz-ban-ner
공식 코드https://github.com/sanmusunrise/ARNs
공식 코드Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region Networks
https://github.com/sberbank-ai/DetIE
공식 코드DetIE: Multilingual Open Information Extraction Inspired by Object Detection
https://github.com/sbnlp/2017BioNLPEvaluation
공식 코드https://github.com/shangjingbo1226/AutoNER
공식 코드Learning Named Entity Tagger using Domain-Specific Dictionary
https://github.com/shannonai/knn-ner
공식 코드$k$NN-NER: Named Entity Recognition with Nearest Neighbor Search
https://github.com/shashank140195/raredis
공식 코드https://github.com/shengwei-tian/dependency-parser-visualization
공식 코드AutoLLM-CARD: Towards a Description and Landscape of Large Language Models
https://github.com/shijie-wu/crosslingual-nlp
공식 코드Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT
https://github.com/shmulvad/zero-for-ner
공식 코드Zero-Shot Learning in Named-Entity Recognition with External Knowledge
https://github.com/shreyashub/BioFLAIR
공식 코드BioFLAIR: Pretrained Pooled Contextualized Embeddings for Biomedical Sequence Labeling Tasks
https://github.com/shubham30vatsal/web-of-law
공식 코드Classification of US Supreme Court Cases using BERT-Based Techniques
https://github.com/shubhamkumarnigam/legaleval23_nonet
공식 코드Nonet at SemEval-2023 Task 6: Methodologies for Legal Evaluation
https://github.com/shuhengl/acl2023_conllpp
공식 코드https://github.com/shuhewang1998/gnn-sl
공식 코드https://github.com/shuhewang1998/gpt-ner
공식 코드https://github.com/shulk97/daniel
공식 코드https://github.com/sislab-unitn/new-event-detection
공식 코드Whats New? Identifying the Unfolding of New Events in Narratives
https://github.com/slanglab/IndiaPoliceEvents
공식 코드https://github.com/sntcristian/zibaldoner
공식 코드Named Entity Recognition in Historical Italian: The Case of Giacomo Leopardi's Zibaldone
https://github.com/social-machines/tweebanknlp
공식 코드https://github.com/solkx/toe
공식 코드https://github.com/sonvx/word2vecVN
공식 코드https://github.com/source-data/soda-data
공식 코드Integrating curation into scientific publishing to train AI models
https://github.com/sreyan88/aclm
공식 코드ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NER
https://github.com/stanford-oval/Lemonade
공식 코드LEMONADE: A Large Multilingual Expert-Annotated Abstractive Event Dataset for the Real World
https://github.com/stanfordnlp/en-worldwide-newswire
공식 코드Do "English" Named Entity Recognizers Work Well on Global Englishes?
https://github.com/stardust-hyx/tabeae
공식 코드https://github.com/stefan-it/historic-domain-adaptation-icdar
공식 코드Data Centric Domain Adaptation for Historical Text with OCR Errors
https://github.com/stefan-it/historic-ner
공식 코드https://github.com/stefanheng/proggen
공식 코드https://github.com/strawberryx/CoFEE
공식 코드https://github.com/suamin/t2ner
공식 코드Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.