Research on author attribution based on core topic

Subjects: Library Science，Information Science >> Information Retrieval submitted time 2023-02-09

MENG Xu XIE Jing LI Chunwang

Abstract：

[Background and purpose] Author recognition is developing towards the use of multilevel features. Compared with stylistic features, thematic features are still a few in the research and application of author recognition, especially for Chinese social media texts. At the same time, the research on the use of topic features focuses more on the innovation of the extraction technology and methods of topic features, but not on the identified topics and the application methods of topic features. Therefore, the basic purpose of this study is to study the use of topic features in the author recognition of Chinese social media texts, and further develop strategies to identify and screen the core topics in the topic features, optimize the use of topic features, so as to improve the use effect of topic features in the author recognition. [Methods] The research first uses the LDA topic model to extract the academic topics and social topics of the candidate authors, and then uses Word2vec to develop a merge screening strategy to identify and represent the core topics, and finally uses N-gram features and similarity calculation to achieve author recognition. [Results] The experimental results showed that the thematic features had a certain positive effect on the author's recognition in the corpus of this study, and the strategies and applications related to the core thematic features proposed in this study could also optimize the use of thematic features.

Peer Review Status:Awaiting Review

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基于关联数据的类簇语义揭示模型研究

Subjects: Library Science，Information Science >> Information Science submitted time 2017-11-08 Cooperative journals: 《数据分析与知识发现》

崔家旺 李春旺

Abstract：【目的】调研基于关联数据揭示类簇内主题词间语义关系的模型和技术方法。【方法】利用Google Scholar、Springer、CNKI 等检索与研究主题相关的文献, 调研分析并梳理当前类簇分析和语义关系揭示相关研究, 构建基于关联数据的类簇语义关系揭示模型, 通过实验验证模型的有效性。【结果】实验结果表明, 利用关联数据可以有效揭示主题词间语义关系, 弥补传统共词聚类分析在语义方面的不足。【局限】受实验数据限制, 目前揭示出的语义关系局限于上下位类关系、类与实例关系和相关关系等类型, 未考虑关联数据质量问题对语义揭示结果造成的影响。【结论】提出的基于关联数据的类簇语义关系揭示模型可以有效揭示主题词间语义关系, 为共词聚类结果的理解和分析提供一种新的方式。

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基于LOD的注释服务技术研究

Subjects: Library Science，Information Science >> Information Science submitted time 2017-05-17

于倩倩 李春旺

Abstract：本文对基于关联开放数据(LOD)进行的文本、图像和视频等Web 资源注释服务的相关技术方法进行了梳理和总结，介绍了注释流程中的关联数据查询、语义消歧技术、关联扩展技术、关联数据过滤技术和关联模型技术，并提出注释服务应用面临的问题。

Peer Review Status:Awaiting Review

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