[1]付学敬,丁肖摇.基于多层次语义感知的中文关系抽取研究[J].计算机技术与发展,2024,34(01):143-149.[doi:10. 3969 / j. issn. 1673-629X. 2024. 01. 021]
　FU Xue-jing,DING Xiao-yao.Research on Chinese Relation Extraction Based on Multi-level Semantic Perception[J].,2024,34(01):143-149.[doi:10. 3969 / j. issn. 1673-629X. 2024. 01. 021]
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基于多层次语义感知的中文关系抽取研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年01期
页码:
143-149
栏目:
人工智能
出版日期:
2024-01-10

文章信息/Info

Title:
Research on Chinese Relation Extraction Based on Multi-level Semantic Perception
文章编号:
1673-629X(2024)01-0143-07
作者:
付学敬1 ; 丁肖摇2
1. 上海市市场监督管理局信息应用研究中心,上海 200032;
2. 战略支援部队信息工程大学,河南 郑州 450000
Author(s):
FU Xue-jing1 ; DING Xiao-yao2
1. Information Application Research Center of Shanghai Municipal Administration for Market Regulation,Shanghai 200032,China;
2. PLA Strategic Support Force Information Engineering University,Zhengzhou 450000,China
关键词:
知识图谱; 中文; 关系抽取; 多层次; 语义感知
Keywords:
knowledge graphs; Chinese; relation extraction; multi-level; semantic perception
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2024. 01. 021
摘要:
关系抽取是构建知识图谱的基础,而中文关系抽取也是关系抽取中的难点问题,现有的中文关系抽取大多采用基于字符特征或者词特征的方法,但是前者无法捕获字符上下文的信息而后者受制于分词质量,导致中文关系抽取的性能较低。 针对该问题,提出了基于多层次语义感知的中文关系抽取模型,该模型利用实体间丰富的语义信息来提高实体对关系预测的性能。多层次语义感知体现在以下三个方面:首先,利用 ERNIE 预训练语言模型将文本信息转化为动态词向量;然后,利用注意力机制增强实体所在句子的语义表示,同时通过外部知识尽可能地消除实体词的中文歧义;最后,将包含多层语义感知的句子表示放入到分类中进行预测。 实验结果表明,所提模型在中文关系抽取的性能上优于已有模型,且更具解释性。
Abstract:
Relation extraction is the basis of constructing knowledge graphs,and Chinese relation extraction is also a difficult problem inrelation extraction. Most existing Chinese relation extraction methods use character-based or word-based features,but the former cannotcapture contextual information of characters and the latter is limited by the quality of word segmentation,resulting in lower performance ofChinese relation extraction. In response to this problem,a Chinese relation extraction model based on multi-level semantic perception isproposed. This model uses rich semantic information between entities to improve the performance of predicting relationships betweenentities. Multi-level semantic perception is reflected in the following three aspects:firstly,text information is transformed into dynamicword vectors using the pre-training language model ERNIE;then,attention mechanism is used to enhance the semantic representation ofthe sentence where the entity is located,while external knowledge is used to eliminate Chinese ambiguity of entity words as much aspossible;finally, the sentence representation containing multi - level semantic perception is put into classification for prediction.Experimental results show that the proposed model outperforms existing models in Chinese relation extraction performance and is more interpretable.

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更新日期/Last Update: 2024-01-10