Few-shot relation extraction (FSRE) is of great importance in long-tail
distribution problem, especially in special domain with low-resource data. Most
existing FSRE algorithms fail to accurately classify the relations merely based
on the information of the sentences together with the recognized entity pairs,
due to limited samples and lack of knowledge. To address this problem, in this
paper, we proposed a novel entity CONCEPT-enhanced FEw-shot Relation Extraction
scheme (ConceptFERE), which introduces the inherent concepts of entities to
provide clues for relation prediction and boost the relations classification
performance. Firstly, a concept-sentence attention module is developed to
select the most appropriate concept from multiple concepts of each entity by
calculating the semantic similarity between sentences and concepts. Secondly, a
self-attention based fusion module is presented to bridge the gap of concept
embedding and sentence embedding from different semantic spaces. Extensive
experiments on the FSRE benchmark dataset FewRel have demonstrated the
effectiveness and the superiority of the proposed ConceptFERE scheme as
compared to the state-of-the-art baselines. Code is available at
this https URL

本文提出了一种新颖的基于实体概念增强的少样本关系提取方案（ConceptFERE），通过计算句子与概念之间的语义相似度，选择最合适的概念，再通过自注意力融合模块建立概念嵌入和句子嵌入之间的联系，提高了关系分类性能。