Papers with Code 아카이브
Relation Extraction
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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/HazyResearch/tanda
공식 코드Learning to Compose Domain-Specific Transformations for Data Augmentation
https://github.com/Hector1993prog/Spanish_PromptORE
공식 코드https://github.com/Hi-ZenanXu/Syntax-Enhanced_Pre-trained_Model
공식 코드https://github.com/Hunter-DDM/cfer-document-level-RE
공식 코드Coarse-to-Fine Entity Representations for Document-level Relation Extraction
https://github.com/IBM/kbqa-relation-linking
공식 코드Leveraging Semantic Parsing for Relation Linking over Knowledge Bases
https://github.com/IBM/regen
공식 코드https://github.com/INK-USC/DualRE
공식 코드Learning Dual Retrieval Module for Semi-supervised Relation Extraction
https://github.com/INK-USC/NERO
공식 코드NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction
https://github.com/INK-USC/NExT
공식 코드https://github.com/INK-USC/shifted-label-distribution
공식 코드https://github.com/Impavidity/relogic
공식 코드Simple BERT Models for Relation Extraction and Semantic Role Labeling
https://github.com/JiaQiSJTU/DialogueRelationClassification
공식 코드DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues
https://github.com/JohnGiorgi/seq2rel-ds
공식 코드A sequence-to-sequence approach for document-level relation extraction
https://github.com/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/1.4.Biomedical_NER_SparkNLP_paper_reproduce.ipynb
공식 코드https://github.com/KLUE-benchmark/KLUE
공식 코드https://github.com/Kaleidophon/deep-significance
공식 코드https://github.com/LittleGuoKe/ConceptFERE
공식 코드https://github.com/Liu-Hy/nlp-contrib-graph
공식 코드https://github.com/LiyuanLucasLiu/ReHession
공식 코드Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach
https://github.com/LorrinWWW/two-are-better-than-one
공식 코드Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders
https://github.com/Lu-Yang666/LDNet
공식 코드Label Drop for Multi-Aspect Relation Modeling in Universal Information Extraction
https://github.com/LuminosoInsight/semeval-discriminatt
공식 코드https://github.com/Mrlyk423/Relation_Extraction
공식 코드Modeling Relation Paths for Representation Learning of Knowledge Bases
https://github.com/MurathanKurfali/multilingual_IDRC
공식 코드Zero-shot transfer for implicit discourse relation classification
https://github.com/MurtyShikhar/ExpBERT
공식 코드ExpBERT: Representation Engineering with Natural Language Explanations
https://github.com/NVIDIA/NeMo
공식 코드https://github.com/NeuralSentinel/DistALANER
공식 코드https://github.com/Nithin-Holla/MetaLifelongLanguage
공식 코드Meta-Learning with Sparse Experience Replay for Lifelong Language Learning
https://github.com/OpenBMB/MiniCPM-o
공식 코드https://github.com/PKU-YuanGroup/Video-LLaVA
공식 코드Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
https://github.com/PKUYeYuan/Constraint-Loss-AAAI-2020
공식 코드Integrating Relation Constraints with Neural Relation Extractors
https://github.com/PaddlePaddle/PaddleOCR
공식 코드https://github.com/PaddlePaddle/Research
공식 코드https://github.com/PlusLabNLP/Deep-Structured-EveEveTemp
공식 코드Deep Structured Neural Network for Event Temporal Relation Extraction
https://github.com/PlusLabNLP/EventPlus
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.