https://github.com/pkuserc/AAAI2021-MIUK-Relation-Extraction
공식 코드Multi-view Inference for Relation Extraction with Uncertain Knowledge
Papers with Code 아카이브
Relation Extraction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 51
- 보존된 평가 기록
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Relation Extraction is the task of predicting attributes and relations for entities in a sentence. For example, given a sentence “Barack Obama was born in Honolulu, Hawaii.”, a relation classifier aims at predicting the relation of “bornInCity”. Relation Extraction is the key component for building relation knowledge graphs, and it is of crucial significance to natural language processing applications such as structured search, sentiment analysis, question answering, and summarization.
Source: Deep Residual Learning for Weakly-Supervised Relation Extraction
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/pkuserc/rela
공식 코드Sequence Generation with Label Augmentation for Relation Extraction
https://github.com/pluslabnlp/econet
공식 코드ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning
https://github.com/pluslabnlp/zero_shot_cqa
공식 코드Zero-shot Commonsense Question Answering with Cloze Translation and Consistency Optimization
https://github.com/pnborchert/core
공식 코드CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation
https://github.com/pnborchert/multirep
공식 코드https://github.com/ppuliu/GloRE
공식 코드https://github.com/prastunlp/DualDec
공식 코드A Cascade Dual-Decoder Model for Joint Entity and Relation Extraction
https://github.com/princeton-nlp/PURE
공식 코드A Frustratingly Easy Approach for Entity and Relation Extraction
https://github.com/psunlpgroup/fish-dip
공식 코드Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning
https://github.com/ptarau/DeepRank
공식 코드https://github.com/pytorch/fairseq
공식 코드VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding
https://github.com/qingwang-isu/augure
공식 코드Improving Unsupervised Relation Extraction by Augmenting Diverse Sentence Pairs
https://github.com/qingwang-isu/mixore
공식 코드Towards a More Generalized Approach in Open Relation Extraction
https://github.com/qipeng/gcn-over-pruned-trees
공식 코드Graph Convolution over Pruned Dependency Trees Improves Relation Extraction
https://github.com/qisun123/ugdre
공식 코드Uncertainty Guided Label Denoising for Document-level Distant Relation Extraction
https://github.com/qwenlm/qwen2-vl
공식 코드Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
https://github.com/rahular/coref-rl
공식 코드Rewarding Coreference Resolvers for Being Consistent with World Knowledge
https://github.com/rajatkoner08/rtn
공식 코드https://github.com/raspberryice/type-discovery-abs
공식 코드Open Relation and Event Type Discovery with Type Abstraction
https://github.com/redreamality/RERE-relation-extraction
공식 코드Revisiting the Negative Data of Distantly Supervised Relation Extraction
https://github.com/renll/HySPA
공식 코드HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction
https://github.com/renzelou/more
공식 코드MORE: A Metric Learning Based Framework for Open-domain Relation Extraction
https://github.com/rgop13/GRASP
공식 코드GRASP: Guiding model with RelAtional Semantics using Prompt for Dialogue Relation Extraction
https://github.com/richielee93/eder
공식 코드EDeR: A Dataset for Exploring Dependency Relations Between Events
https://github.com/ridiculouz/llmaaa
공식 코드https://github.com/ritvikshrivastava/multitask_transformers
공식 코드"Laughing at you or with you": The Role of Sarcasm in Shaping the Disagreement Space
https://github.com/ronentk/textlabs-xwlp-data
공식 코드Process-Level Representation of Scientific Protocols with Interactive Annotation
https://github.com/rtmaww/SENT
공식 코드SENT: Sentence-level Distant Relation Extraction via Negative Training
https://github.com/rubenkruiper/FOBIE
공식 코드In Layman's Terms: Semi-Open Relation Extraction from Scientific Texts
https://github.com/rudongyu/logire
공식 코드https://github.com/rujunhan/EMNLP-2020
공식 코드Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction
https://github.com/s-nlp/konstruktor
공식 코드Konstruktor: A Strong Baseline for Simple Knowledge Graph Question Answering
https://github.com/sa5r/mrca
공식 코드Multiple Relations Classification using Imbalanced Predictions Adaptation
https://github.com/saintfe/rdr
공식 코드Do Models Learn the Directionality of Relations? A New Evaluation: Relation Direction Recognition
https://github.com/salesforce/DocNLI
공식 코드DocNLI: A Large-scale Dataset for Document-level Natural Language Inference
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.