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A Joint Entity-Relation Detection and Generalization Method Based on Syntax and Semantics for Chinese Intangible Cultural Heritage Texts

Journal on Computing and Cultural Heritage (JOCCH)(2024)SCI 4区

Nanjing Univ

Cited 0|Views74
Abstract
Annotation of a natural language corpus not only facilitates researchers in extracting knowledge from it but also helps achieve deeper mining of the corpus. However, an annotated corpus in the humanities knowledge domain is lacking. In addition, the semantic annotation of humanities texts is difficult, because it requires a high domain background for researchers and even requires the participation of domain experts. Based on this, this study proposes a method for detecting entities and relations in a domain that lacks an annotated corpus, as well as provides a referenceable idea for constructing conceptual models based on textual instances. Based on syntactic and semantic features, this study proposes SPO triple recognition rules from the perspective of giving priority to predicates and generalization rules from the perspective of a triple’s content and the meaning of its predicate. The recognition rules are used to extract text-descriptive SPO triples centered on predicates. After clustering and adjusting triples, the generalization rules proposed in this study are used to obtain coarse-grained entities and relations, and then form a conceptual model. This study recognizes SPO triples with high precision and summarization from descriptive texts, generalizes them, and then forms a domain conceptual model. Our proposed method provides a research idea for entity-relation detection in a domain with a missing annotated corpus, and the formed domain conceptual model provides a reference for building a domain Linked Data Graph. The feasibility of the method is verified through practice on texts related to the four traditional Chinese festivals.
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Digital humanity,intangible cultural heritage,relation extraction,Linked Data
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要点】:本文针对缺乏注释语料库的人文知识领域,提出一种基于语法和语义特征联合实体-关系检测与泛化方法,用于构建领域概念模型,提高文本描述的SPO三元组识别精度和泛化能力。

方法】:研究基于优先考虑谓词的视角提出SPO三元组识别规则,以及从三元组内容和谓词含义出发的泛化规则,通过提取以谓词为中心的描述性文本SPO三元组,经聚类调整后,利用泛化规则获得粗粒度的实体和关系,进而形成概念模型。

实验】:研究通过在与中国四大传统节日相关的文本上进行实践,验证了方法的可行性,使用的数据集为与四大传统节日相关的文本数据,实验结果显示出高精度和泛化能力。