Papers with Code 아카이브
Relation Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 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/mickeystroller/R-BERT
Enriching Pre-trained Language Model with Entity Information for Relation Classification
https://github.com/microsoft/K-Adapter
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
https://github.com/microsoft/TUTA_table_understanding
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks
https://github.com/microsoft/hitab
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks
https://github.com/mindspore-ai/models/tree/master/research/nlp/luke
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/monologg/R-BERT
Enriching Pre-trained Language Model with Entity Information for Relation Classification
https://github.com/onehaitao/CR-CNN-relation-extraction
Classifying Relations by Ranking with Convolutional Neural Networks
https://github.com/onehaitao/R-BERT-relation-extraction
Enriching Pre-trained Language Model with Entity Information for Relation Classification
https://github.com/plkmo/BERT-Relation-Extraction
Matching the Blanks: Distributional Similarity for Relation Learning
https://github.com/pwc-1/Paper-10/tree/main/tapex
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks
https://github.com/pwc-1/Paper-9/tree/main/luke
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/roomylee/entity-aware-relation-classification
https://github.com/runqitian/CR-CNN
Classifying Relations by Ranking with Convolutional Neural Networks
https://github.com/shmulvad/zero-for-ner
LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
https://github.com/stevekgyang/sccl
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
https://github.com/tticoin/LSTM-ER
End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
https://github.com/uf-hobi-informatics-lab/ClinicalTransformerRelationExtraction
Matching the Blanks: Distributional Similarity for Relation Learning
https://github.com/wang-h/bert-relation-classification
Enriching Pre-trained Language Model with Entity Information for Relation Classification
https://github.com/wooseok-AI/Korean_e2e_CR_BERT
SpanBERT: Improving Pre-training by Representing and Predicting Spans
https://github.com/yangyucheng000/Paper-3/tree/main/SpanQualifier
Span-based Joint Entity and Relation Extraction with Transformer Pre-training
https://github.com/yi-han/BERT_Relation_Extraction
Matching the Blanks: Distributional Similarity for Relation Learning
https://github.com/zixinzeng-jennifer/spanbert_trans
SpanBERT: Improving Pre-training by Representing and Predicting Spans
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.