Entity Concept-enhanced Few-shot Relation Extraction (2021)

Abstract: 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 https://github.com/LittleGuoKe/ConceptFERE .

## introduction relation extraction (re) is a fundamental task for knowledge graph construction and inference, which however often encounters challenges of longtail distribution and low-resource data, especially in practical applications including medical or public security fields. in this case, it is difficult for * corresponding author existing re models to learn effective classifiers @xcite . therefore, fsre has become a hot topic in both academia and industry. existing fsre methods can be roughly divided into two categories according to the type of adopted training data. the models of the first category only uses the plain text data, without any external information. the representative siamese @xcite and prototypical @xcite network in metric learning are used in the fsre task to learn representation and metric function. bert-pair @xcite ) pairs up all supporting instances with each query instance, and predicts whether the pairs are of the same category, which can be regarded as a variant of the prototypical network. gao @xcite and ye @xcite add the attention mechanism to enhance the prototype network. in order to alleviate the problem of insufficient training data, mick @xcite learns general language rules and grammatical knowledge from cross-domain datasets. wang @xcite proposes the cteg model to solve the relation confusion problem of fsre. cong @xcite proposes an inductive clustering based framework, dafec, to solve the problem of domain adaptation in fsre. since the information of the plain text is limited in fsre scenarios, the performance gain is marginal. thus, the algorithms in the second category introduce external information, to compensate the limited information in fsre, so as to enhance the performance. in order to improve the model's generalization ability for new relations, qu @xcite come from wikidata. foot_0 td-proto @xcite ) introduces text descriptions of entities and relations from wikidata to enhance the prototype network and provide clues for relation prediction. although the introduction of knowledge of text description can provide external information for fsre and achieve state-of-the-art performance, td-proto only introduces one text description in wikidata for each entity. however, this might suffer from the mismatching between entity and text description and leads to the degraded performance. besides, since the text description for each entity is often relatively long, it is not a easy job to extract the most useful information within the long text description. in contrast to the long text descriptions, the concept is an intuitive and concise description of an entity and can be readily obtained from concept databases, like yago3, conceptnet and concept graph, etc. besides, the concept is more abstract than the specific text description for each entity, which is an idea compensation to the limited information in fsre scenarios. as shown in to address the above challenges, we propose a novel entity concept-enhanced few-shot relation extraction scheme (conceptfere), which introduces the entity concept to provide effective clues for relation prediction. firstly, as shown in experimental results on benchmark dataset show that our method achieves state-of-the-art fsre performance. ## conclusion in this paper, we have studied the fsre task and presented a novel entity concept-enhanced fsre scheme (conceptfere). the concept-sentence attention module was designed to select the appropriate concept from multiple concepts corresponding to each entity, and the fusion module was designed to integrate the concept and sentence semantically at the word-level. the experimental results have demonstrated the effectiveness of our method against state-of-the-art algorithms. as a future work, the commonsense knowledge of the concepts as well as the possible relations between them will be explicitly considered to further enhance the fsre performance.