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| 基于实体语义扩展的跨境民族文化文本检索 | |
| 引用本文： | 毛存礼,郝鹏鹏,雷雄丽,王斌,王红斌,张亚飞.基于实体语义扩展的跨境民族文化文本检索[J].中文信息学报,2022,36(11):101-109. |
| 作者姓名： | 毛存礼 郝鹏鹏 雷雄丽 王斌 王红斌 张亚飞 |
| 作者单位： | 1.昆明理工大学 信息工程与自动化学院,云南 昆明 650000; 2.昆明理工大学 云南省人工智能重点实验室,云南 昆明 650000; 3.昆明冶金高等专科学校,云南 昆明 650000 |
| 基金项目： | 国家自然科学基金(61732005;61866019,61761026,61972186);云南省应用基础研究计划重点项目(2019FA023);云南省中青年学术和技术带头人后备人才项目(2019HB006);云南省重大科技专项计划项目(202103AA080015,202002AD080001) |
| 摘 要： | 由于跨境民族相关的文化实体常出现相同实体具有不同名称表达的情况，使用当前主流的文本检索方法在跨境民族文化数据集上将面临语义稀疏的问题。该文提出一种基于实体语义扩展的跨境民族文化检索方法，利用跨境民族文化知识图谱，以知识三元组的形式将跨境民族文化之间的实体关联起来，并添加实体类别标签，以此缓解跨境民族文化实体中语义信息不充分的问题。通过TransH模型对实体及扩展语义信息进行向量化表示，融合到查询文本中进行语义增强，以此提升跨境民族文化文本检索的准确性。实验结果表明，该方法比基线模型提高了5.4%。 |
| 关 键 词： | 文本检索 跨境民族文化 知识图谱 实体语义扩展 |
| 收稿时间： | 2021-09-19 |
Entity Semantic Extension Based Culture Text Retrieval for Cross-Country Ethnic Group |
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| MAO Cunli,HAO Pengpeng,LEI Xiongli,WANG Bin,WANG Hongbin,ZHANG Yafei.Entity Semantic Extension Based Culture Text Retrieval for Cross-Country Ethnic Group[J].Journal of Chinese Information Processing,2022,36(11):101-109. | |
| Authors: | MAO Cunli HAO Pengpeng LEI Xiongli WANG Bin WANG Hongbin ZHANG Yafei |
| Affiliation: | 1.Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan 650000, China;2.Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, Yunnan 650000, China;3.Kunming Metallurgical College, Kunming, Yunnan 650000, China |
| Abstract: | To deal with the semantic sparsity caused by same entities in different forms in the culture of cross-border ethnic groups, this paper proposes a cross-border ethnic culture retrieval method based on entity semantic expansion. It uses the cross-border ethnic cultural knowledge map to associate the entities between various culture texts in the form of knowledge triples with addtional entity category tags. The TransH model is applied to represent entities and their extended semantic information, which is integrated into the query as kind of semantic enhancement. Experimental results show that the proposed method is 5.4% higher than the baseline model. |
| Keywords: | text retrieval cross border national culture knowledge graph entity semantic extension |
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