https://github.com/PlusLabNLP/TempGen
공식 코드Document-level Entity-based Extraction as Template Generation
Papers with Code 아카이브
Relation Extraction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/RaghavendraCh/RelationExtraction_keras
공식 코드A hybrid deep learning approach for medical relation extraction
https://github.com/Receiling/UniRE
공식 코드https://github.com/RidongHan/Correl-RE
공식 코드Document-level Relation Extraction with Relation Correlations
https://github.com/RidongHan/RHIA-EOP
공식 코드https://github.com/SJTU-lqiu/QA4IE
공식 코드QA4IE: A Question Answering based Framework for Information Extraction
https://github.com/SapienzaNLP/relik
공식 코드https://github.com/Spico197/NYT-H
공식 코드Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction
https://github.com/SriramPingali/MagBERT
공식 코드Multimodal Graph-based Transformer Framework for Biomedical Relation Extraction
https://github.com/SuLab/crowdflower_relation_verification
공식 코드Exposing ambiguities in a relation-extraction gold standard with crowdsourcing
https://github.com/THU-BPM/Env-DocRE
공식 코드On the Robustness of Document-Level Relation Extraction Models to Entity Name Variations
https://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/TangJiaLong/Knowledge-Projection-for-ERE
공식 코드From Discourse to Narrative: Knowledge Projection for Event Relation Extraction
https://github.com/Tiiiger/SGC
공식 코드https://github.com/Upaya07/GPT-FinRE
공식 코드GPT-FinRE: In-context Learning for Financial Relation Extraction using Large Language Models
https://github.com/Usprogis/Latvian-Twitter-Eater-Corpus
공식 코드https://github.com/WeiKangda/LLM-ERE
공식 코드Are LLMs Good Annotators for Discourse-level Event Relation Extraction?
https://github.com/WenjieDu/PyPOTS
공식 코드https://github.com/WindChimeRan/CopyMTL
공식 코드CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning
https://github.com/WindChimeRan/OpenJERE
공식 코드Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction
https://github.com/XueFuzhao/GDPNet
공식 코드GDPNet: Refining Latent Multi-View Graph for Relation Extraction
https://github.com/YangLi1221/CoRA
공식 코드Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention
https://github.com/YoumiMa/TablERT
공식 코드https://github.com/YujiaBao/Distributional-Signatures
공식 코드https://github.com/YuxingLu613/KARMA
공식 코드KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph Enrichment
https://github.com/ZeningLin/PEneo
공식 코드https://github.com/ZhixiuYe/Intra-Bag-and-Inter-Bag-Attentions
공식 코드Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions
https://github.com/ZhixiuYe/MLMAN
공식 코드Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification
https://github.com/aailabkaist/reic
공식 코드Reward-based Input Construction for Cross-document Relation Extraction
https://github.com/ab-10/Hawk
공식 코드https://github.com/ac-zyx/rocore
공식 코드A Relation-Oriented Clustering Method for Open Relation Extraction
https://github.com/accord-project/code-accord
공식 코드https://github.com/acddca/mixred
공식 코드https://github.com/akalino/semantic-structural-sentences
공식 코드A Comparative Study on Structural and Semantic Properties of Sentence Embeddings
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.