信息安全领域内实体共指消解技术研究

张晗; 胡永进; 郭渊博; 陈吉成

doi:10.11959/j.issn.1000-436x.2020033

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信息安全领域内实体共指消解技术研究

学术论文 | 更新时间：2024-06-05

- 信息安全领域内实体共指消解技术研究
- Research on coreference resolution technology of entity in information security
- 通信学报 2020年41卷第2期页码：165-175
- 作者机构：
  
  1. 信息工程大学密码工程学院，河南郑州 450001
  2. 郑州大学软件学院，河南郑州 450000
  3. 信息工程大学信息技术研究所，河南郑州 450001
- 作者简介：
  
  [ "张晗（1985- ），女，河南项城人，信息工程大学博士生，主要研究方向为自然语言处理、信息安全" ]
  [ "胡永进（1981- ），男，山东潍坊人，信息工程大学讲师，主要研究方向为主动防御、态势感知" ]
  [ "郭渊博（1975- ），男，陕西周至人，博士，信息工程大学教授、博士生导师，主要研究方向为大数据安全、态势感知" ]
  [ "陈吉成（1984- ），男，江苏涟水人，信息工程大学博士生，主要研究方向为复杂网络、信息内容安全" ]
- 基金信息：
  
  国家自然科学基金资助项目(61501515);河南省重点科技攻关基金资助项目(172102210002);郑州大学青年骨干教师基金资助项目(2017ZDGGJS048)
- DOI：10.11959/j.issn.1000-436x.2020033
  中图分类号： TP393
- 网络出版日期：2020-02，
  
  纸质出版日期：2020-02-25
- 稿件说明：
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张晗, 胡永进, 郭渊博, 等. 信息安全领域内实体共指消解技术研究[J]. 通信学报, 2020,41(2):165-175.

Han ZHANG, Yongjin HU, Yuanbo GUO, et al. Research on coreference resolution technology of entity in information security[J]. Journal on communications, 2020, 41(2): 165-175.
张晗, 胡永进, 郭渊博, 等. 信息安全领域内实体共指消解技术研究[J]. 通信学报, 2020,41(2):165-175. DOI： 10.11959/j.issn.1000-436x.2020033.

Han ZHANG, Yongjin HU, Yuanbo GUO, et al. Research on coreference resolution technology of entity in information security[J]. Journal on communications, 2020, 41(2): 165-175. DOI： 10.11959/j.issn.1000-436x.2020033.

摘要

针对信息安全领域内的共指消解问题，提出了一个混合型方法。该方法在原来BiLSTM-attention-CRF模型的基础上引入领域词典匹配机制，将其与文档层面的注意力机制相结合，作为一种新的基于字典的注意力机制，来解决从文本中提取候选词时对稀有实体以及长度较长的实体识别能力稍弱的问题，并通过总结领域文本特征，将提取出的待消解候选词根据词性分别采用规则与机器学习的方式进行消解，以提高准确性。通过在安全领域数据集的实验，分别从共指消解以及提取候选词并分类2个方面证明了方法的优越性。

Abstract

To solve the problem of coreference resolution in information security

a hybrid method was proposed.Based on the BiLSTM-attention-CRF model

the domain-dictionary matching mechanism was introduced and combined with the attention mechanism at the document level.As a new dictionary-based attention mechanism

the word features were calculated to solve the problem of weak recognition ability of rare entities and entities with long length when extracting candidates from text.And by summarizing the features of the domain texts

the candidates were coreferenced by rules and machine learning according to the part of speech to improve the accuracy.Through the experiments on security data set

the superiority of the method is proved from the aspects of coreference resolution and extraction of candidates from text .

关键词

Keywords

references

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