计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (6A): 240900096-9.doi: 10.11896/jsjkx.240900096

• 人工智能 • 上一篇    下一篇

基于语义增强的装备事件抽取方法

方睿1, 崔良中1, 方圆婧2   

  1. 1 海军工程大学电子工程学院 武汉 430033
    2 联勤保障部队 武汉 430000
  • 出版日期:2025-06-16 发布日期:2025-06-12
  • 通讯作者: 崔良中(szzll@163.com)
  • 作者简介:(jsj_fr8836@163.com)
  • 基金资助:
    装备预先研究项目(30209040702)

Equipment Event Extraction Method Based on Semantic Enhancement

FANG Rui1, CUI Liangzhong1, FANG Yuanjing2   

  1. 1 School of Electronic Engineering,Naval University of Engineering,Wuhan 430033,China
    2 Joint Logistics Support Force,Wuhan 430000,China
  • Online:2025-06-16 Published:2025-06-12
  • About author:FANG Rui,born in 2000,postgraduate. His main research interests include na-tural language processing,knowledge graph,etc.
    CUI Liangzhong,born in 1979,Ph.D,associate professor. His main research interests include knowledge graph,data mining and analysis,etc.
  • Supported by:
    Equipment Advanced Research Project(30209040702).

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摘要： 信息时代下,装备领域的数据量急剧增长,使得论证人员难以高效地从中获取关键信息,进而支持相应的数据分析和论证工作。针对装备领域事件抽取事件论元边界模糊的问题,提出了一种基于语义增强的装备事件抽取方法。该方法利用装备领域的专业术语和词汇信息,构建领域词向量,并设计能够兼容和整合不同粒度语义信息的模型结构,将装备领域词向量与预训练模型ERNIE生成的字符向量进行融合,将专业术语知识和通用语言理解能力相结合,实现更全面的语义信息捕捉,增强模型对装备领域文本语义的理解,从而提升模型对事件论元边界的识别能力。实验结果表明,该方法在装备领域数据集上取得了优于基线方法的F1值,相比CK-BERT模型F1值提升了3.83%;在公开数据集ACE2005上进行的实验验证了其能有效提升装备领域事件要素抽取的性能。

关键词: 装备领域, 事件抽取, 语义增强, 领域词向量, 预训练模型

Abstract: In the information age,the volume of data in the equipment domain has surged dramatically,making it challenging for analysts to efficientlyextract critical information to support relevant data analyses and arguments. To address the issue of ambi-guous event argument boundaries in the extraction of events within the equipment sector,a semantic-enhanced event extraction method is proposed. This method utilizes specialized terminology and vocabulary information in the equipment domain to construct domain word vectors,and designs a model structure that can be compatible with and integrate semantic information of different granularity,fuses the equipment domain word vectors with character vectors generated by ERNIE of the pre-trained mo-del,combines the knowledge of specialized terminology with the ability of general language comprehension,and realizes a more comprehensive capturing of semantic information that enhances the model’s understanding of the textual semantics of the equipment domain,so as to improve the model’s ability to recognize the boundaries of the event thesis elements.Experimental results demonstrate that,on the equipment domain dataset,the proposed method’s F1 values outperforms baseline approaches,with an improvement of 3.83% compared to the CK-BERT model,and has been validated on the public dataset ACE2005,thereby effectively improving performance in the extraction of event elements in the equipment domain.

Key words: Equipment domain, Event extraction, Semantic enhancement, Domain-specific word vector, Pre-trained model

中图分类号: 

  • TP391
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