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approfondimento - Few-shot Relation Extraction
Few-Shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin: A Novel Approach to Identifying Known and Unknown Relations in Low-Resource Settings
This research paper introduces GPAM, a novel framework for few-shot relation extraction with NOTA (none-of-the-above), addressing the challenges of limited data and unknown relation classification by employing Gaussian prototype and adaptive margin techniques.
Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Comprehensive Evaluation
This work introduces a meta dataset for few-shot relation extraction that captures realistic real-world scenarios, and conducts a comprehensive evaluation of six recent few-shot relation extraction methods, revealing the need for substantial future research in this domain.
Explicit Evidence Reasoning for Few-shot Relation Extraction with Chain of Thought Approach
Large language models can achieve competitive performance in few-shot relation extraction tasks using the CoT-ER approach, which incorporates explicit evidence reasoning.
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