一种用于实体关系三元组抽取的位置辅助分步标记方法

Position-Aware Stepwise Tagging Method for Triples Extraction of Entity-Relationship

【目的】针对非结构化文本中的三元组抽取问题,设计能够提升抽取效果并适用于重叠场景的联合抽取模型。【方法】设计一种基于位置感知的分步标记方法,首先通过标记头尾位置确定主实体,接着在逐一预设的关系属性下标记相应的客实体。为提升抽取效果,在标记过程中引入三重位置辅助信息,并结合前序结果及注意力机制共享底层编码。【结果】在中文公开数据集DuIE上进行实验,结果表明所提方法优于其他基线方法,F1值达0.886。此外,还通过消融研究对各组件的有效性进行验证。【局限】标记机制和匹配模式尚未考虑到偶现的嵌套实体问题,有待进一步探索。【结论】所提联合抽取方法可以妥善解决包括重叠场景在内的三元组抽取问题,模型采用的位置辅助设计对后续研究有借鉴意义。

[Objective]This paper designs a joint model for overlapping scenes,aiming to effectively extract triples from unstructured texts.[Methods]We designed a tagging method with position-aware stepwise technique.First,the main entities were determined by tagging their start and end positions.Then,we tagged the corresponding objects under each predefined relations.We also added multiple position-aware information to the tagging procedures.Finally,we shared the encoded sequences with the pre-order results and the attention mechanism.[Results]We examined our new model with DuIE,a Chinese public dataset.The performance of our method is better than those of the baseline models,with an F1 value of 0.886.We also verified the effectiveness of the model’s components through ablation studies.[Limitations]More research is needed to investigate the occasionally nested entities.[Conclusions]The proposed method could effectively address the issues facing triple extraction for overlapping scenes,and provide reference for future studies.