Few-Shot Relation Classification
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Benchmarks
Most implemented
Matching the Blanks: Distributional Similarity for Relation Learning
Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning
CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation
Dependency-aware Prototype Learning for Few-shot Relation Classification
Few-Shot Document-Level Relation Extraction
Towards Realistic Few-Shot Relation Extraction
Papers
Diversity Over Quantity: A Lesson From Few Shot Relation Classification
In few-shot relation classification (FSRC), models must generalize to novel relations with only a few labeled examples. While much of the recent progress in NLP has focused on scaling data size, we argue that diversity i…
DiversityFew-Shot LearningFew-Shot Relation ClassificationRelation+1Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features
Relation classification (RC) plays a pivotal role in both natural language understanding and knowledge graph completion. It is generally formulated as a task to recognize the relationship between two entities of interest…
Feature EngineeringFew-Shot LearningFew-Shot Relation ClassificationKnowledge Graph Completion+5Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning
Differentiating relationships between entity pairs with limited labeled instances poses a significant challenge in few-shot relation classification. Representations of textual data extract rich information spanning the d…
ClassificationContrastive LearningFew-Shot Relation ClassificationRelation+3Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification
This paper introduces a novel neuro-symbolic architecture for relation classification (RC) that combines rule-based methods with contemporary deep learning techniques. This approach capitalizes on the strengths of both p…
Few-Shot Relation ClassificationRelationRelation ClassificationSemantic Text Matching+1CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation
We introduce CORE, a dataset for few-shot relation classification (RC) focused on company relations and business entities. CORE includes 4,708 instances of 12 relation types with corresponding textual evidence extracted …
Domain AdaptationFew-Shot Relation ClassificationRelationRelation Classification+1Dependency-aware Prototype Learning for Few-shot Relation Classification
Few-shot relation classification aims to classify the relation type between two given entities in a sentence by training with a few labeled instances for each relation. However, most of existing models fail to distinguis…
ClassificationFew-Shot Relation ClassificationRelationRelation Classification+1