基于关系冗余度的少样本实体关系自适应抽取

Adaptive Extraction of Entity Relationship with Few Samples Based on Relationship Redundancy

医学文本中丰富的医学知识可为构建医学知识图谱提供数据支撑,但医学文本中存在部分知识文本数量较少,导致了知识分布不平衡、循证类知识样本少等现实问题,且现有实体关系抽取模型对关系冗余、实体重叠等问题并没有很好的解决方案。论文针对上述问题,提出了一种基于关系冗余度的少样本实体关系自适应抽取模型,该模型弥补了现有抽取模型过度依赖大量标注语料、无法解决实体重叠等不足。使用医学相关文本展开实验,结果表明该模型较现有抽取模型F1性能提高了4.9%。

The rich medical knowledge in medical texts can provide data support for the construction of medical knowledge map,but there are a few knowledge texts in medical texts,which leads to practical problems such as unbalanced knowledge distribution and few evidence-based knowledge samples,and the existing entity relationship extraction model has no good solution to the problems of relationship redundancy and entity overlap.Aiming at the above problems,this paper proposes an adaptive entity relationship extraction model with few samples based on relationship redundancy,which makes up for the shortcomings of the existing extraction models,such as over reliance on a large number of labeled corpus and inability to solve entity overlap.The experimental results show that the performance of this model is improved by 4.9%compared with the existing extraction model F1.