Papers with Code 아카이브
Relation Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 8
- 보존된 평가 기록
- 33
Relation Classification is the task of identifying the semantic relation holding between two nominal entities in text.
Source: Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/CrowdTruth/Open-Domain-Relation-Extraction
공식 코드False Positive and Cross-relation Signals in Distant Supervision Data
https://github.com/DFKI-NLP/DISTRE
공식 코드Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction
https://github.com/DS3Lab/ner-at-first-sight
공식 코드https://github.com/DS3Lab/zuco-nlp
공식 코드https://github.com/GanjinZero/CODER
공식 코드CODER: Knowledge infused cross-lingual medical term embedding for term normalization
https://github.com/Georgetown-IR-Lab/semeval2018-task7
공식 코드GU IRLAB at SemEval-2018 Task 7: Tree-LSTMs for Scientific Relation Classification
https://github.com/HKUST-KnowComp/BMGF-RoBERTa
공식 코드https://github.com/Hi-ZenanXu/Syntax-Enhanced_Pre-trained_Model
공식 코드https://github.com/JiaQiSJTU/DialogueRelationClassification
공식 코드DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues
https://github.com/Kaleidophon/deep-significance
공식 코드https://github.com/MurathanKurfali/multilingual_IDRC
공식 코드Zero-shot transfer for implicit discourse relation classification
https://github.com/Receiling/UniRE
공식 코드https://github.com/THU-BPM/MetaSRE
공식 코드Semi-supervised Relation Extraction via Incremental Meta Self-Training
https://github.com/THU-BPM/SelfORE
공식 코드SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction
https://github.com/THU-KEG/KEPLER
공식 코드KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
https://github.com/Usprogis/Latvian-Twitter-Eater-Corpus
공식 코드https://github.com/YujiaBao/Distributional-Signatures
공식 코드https://github.com/ZhixiuYe/MLMAN
공식 코드Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification
https://github.com/allenai/kb
공식 코드https://github.com/amazon-research/tanl
공식 코드Structured Prediction as Translation between Augmented Natural Languages
https://github.com/amazon-science/e2e-docie
공식 코드REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
https://github.com/asahi417/relbert
공식 코드Distilling Relation Embeddings from Pre-trained Language Models
https://github.com/avramandrei/DeftEval
공식 코드UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition Extraction
https://github.com/banglab-udem-mila/nlp4matsci-acl23
공식 코드https://github.com/bionlproc/end-to-end-combdrugext
공식 코드End-to-End $n$-ary Relation Extraction for Combination Drug Therapies
https://github.com/bnlnlp/ppi-relation-extraction
공식 코드https://github.com/boun-tabi/RELX
공식 코드The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification
https://github.com/brownfortress/llms-trc
공식 코드Will LLMs Replace the Encoder-Only Models in Temporal Relation Classification?
https://github.com/cgraywang/deepex
공식 코드Zero-Shot Information Extraction as a Unified Text-to-Triple Translation
https://github.com/cgraywang/deepstruct
공식 코드DeepStruct: Pretraining of Language Models for Structure Prediction
https://github.com/clulab/releases
공식 코드From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction
https://github.com/cpaaax/multimodal_cast
공식 코드Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal Classification
https://github.com/datquocnguyen/jointRE
공식 코드End-to-end neural relation extraction using deep biaffine attention
https://github.com/declare-lab/relationprompt
공식 코드https://github.com/declare-lab/vip
공식 코드Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.