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Received:June 01, 2024 Revised:June 26, 2024
Received:June 01, 2024 Revised:June 26, 2024
中文摘要: 针对现有的小样本关系三元组抽取方法难以解决单句话中存在多个三元组以及未考虑支持集和查询集之间语义相似性等问题, 提出了一种基于模块转移和语义相似性推断的小样本关系三元组抽取方法. 该方法采用一种在关系抽取、实体识别和三元组判别这3个模块不断转移的机制, 高效地提取出查询实例中存在的多个关系三元组. 在关系抽取部分, 将BiLSTM和注意力机制相融合, 以更好地捕捉应急预案文本的序列信息. 此外, 在实体识别部分设计了一种基于语义相似性推断的方法识别句子中存在的应急组织机构实体. 最终, 在应急预案领域数据集ERPs+上进行了大量的实验. 实验结果显示, 相较于其他基线模型, 所提模型更适应于应急预案领域的关系三元组抽取任务.
Abstract:Existing few-shot relational triple extraction methods often struggle with handling multiple triples in a single sentence and fail to consider the semantic similarity between the support set and the query set. To address these issues, this study proposes a few-shot relational triple extraction method based on module transfer and semantic similarity inference. The method uses a mechanism that constantly transfers among three modules, namely relation extraction, entity recognition, and triple discrimination, to extract multiple relational triples efficiently from a query instance. In the relation extraction module, BiLSTM and a self-attention mechanism are integrated to better capture the sequence information of the emergency plan text. In addition, a method based on semantic similarity inference is designed to recognize emergency organizational entities in sentences. Finally, extensive experiments are conducted on ERPs+, a dataset for emergency response plans. Experimental results show that the proposed model is more suitable for relational triple extraction in the field of emergency plans compared with other baseline models.
keywords: emergency response planning relational triple extraction few-shot learning module transfer semantic similarity
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基金项目:科技创新2030—“新一代人工智能”重大项目(2022ZD0119500); 山东省自然科学基金(ZR2022MF319); 山东科技大学青年教师教学拔尖人才培养基金(BJ20211110)
引用文本:
刘彤,刘炳霄,倪维健.基于模块转移和语义相似性推断的小样本关系三元组抽取.计算机系统应用,,():1-10
LIU Tong,LIU Bing-Xiao,NI Wei-Jian.Few-shot Relational Triple Extraction Based on Module Transfer and Semantic Similarity Inference.COMPUTER SYSTEMS APPLICATIONS,,():1-10
刘彤,刘炳霄,倪维健.基于模块转移和语义相似性推断的小样本关系三元组抽取.计算机系统应用,,():1-10
LIU Tong,LIU Bing-Xiao,NI Wei-Jian.Few-shot Relational Triple Extraction Based on Module Transfer and Semantic Similarity Inference.COMPUTER SYSTEMS APPLICATIONS,,():1-10