基于机器阅读理解的非遗文本实体抽取研究

Extracting Entities from Intangible Cultural Heritage Texts Based on Machine Reading Comprehension

【目的】针对当前非遗文本实体抽取研究的不足,提出以机器阅读理解方法为基础,通过问答的方式对非遗文本中的实体进行抽取。【方法】构建非遗实体敏感的注意力机制,用于捕捉非遗文本上下文同问题之间的联系,使模型关注同问题相关的非遗实体,并建立非遗文本实体抽取模型ICHQA。【结果】将ICHQA模型在标注的非遗语料库中进行实证研究,并同相关基线模型进行对比,结果表明ICHQA在F1指标中表现最优,达87.139%。为凸显模型的优势和增强可解释性,本文还展开了消融实验并对模型输出进行了可视化。【局限】本文提出的模型仅在非遗语料库中进行验证,泛化性测试不够。【结论】利用机器阅读理解进行非遗实体抽取,能够有效利用实体标签的语义特征,提升实体抽取的效果。

[Objective] This paper proposes a Question-Answering(QA) model based on machine reading comprehension(MRC) to extract entities from Intangible Cultural Heritage(ICH) texts. [Methods] First, we constructed an ICH entity sensitive attention mechanism, which captured the interaction between contexts and questions. The mechanism also helps our model focus on questions and related ICH entities. Then, we built the ICHQA model for entity extraction. [Results] We examined the ICHQA model with the ICH corpus. The ICHQA’s F1 value reached 87.139%, which was better than the existing models. We also performed ablation studies and visualized outputs of the ICHQA. [Limitations] More research is needed to examine the proposed model with other corpus from digital humanities. [Conclusions] The proposed model could effectively extract ICH entities.