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Integrating Deep Learning Approaches for Identifying News Reprint Relation

文献类型：期刊论文

作者Luo, Yin1; Wang, Fangfang2,3; Chen, Jun4; Wang, Lei3,5; Zeng, Daniel Dajun3,6,7
刊名IEEE ACCESS
出版日期2018
卷号6页码:72163-72172
关键词Deep learning diffusion pattern news reprint relation identification semantic relevance word embedding
ISSN号2169-3536
DOI10.1109/ACCESS.2018.2882624
通讯作者Wang, Fangfang(fangfang.wang@ia.ac.cn)
英文摘要With the rapid development of big data and new media technologies, a large amount of original news is generated and reprinted on the Internet via news portals. Identifying news reprint relations is of great importance for the analysis of news diffusion patterns and copyright protection. However, the amount of news data on the Internet creates a huge challenge for efficiently identifying news reprint relation. Some existing studies focus on computing the similarity of the full text of news reports, which is not always effective, because some reprints only excerpt some sentences of the original news reports. The core challenge of improving identification accuracy is excavating the potential semantic relevance between news articles at the sentence level. Inspired by deep learning and semantic-based text representation models, this paper proposes an approach for identifying news reprint relation by integrating deep learning approaches. First, news reports that are not related to the topic of the original news report are removed via topic correlation mining. Then, the potential semantic relevance is excavated at the sentence level through the integration of semantic analysis methods, and reprint relations are identified between news reports. The performance of the approach is empirically evaluated using a real-world dataset. Experimental results show that the semantic analysis model integration allows us to mine in-depth semantic associations between news stories and accurately identify news reprint relations. These results benefit news diffusion pattern analysis and copyright protection.
WOS关键词MEDIA
资助项目National Key R&D Program of China[2016QY02D0305] ; National Natural Science Foundation of China[61671450] ; National Natural Science Foundation of China[71621002] ; Key Research Program of the Chinese Academy of Sciences[ZDRW-XH-2017-3]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000453702400001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Key Research Program of the Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/25651]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者Wang, Fangfang
作者单位1.Beijing Inst Technol, Sch Management & Econ, Beijing 100081, Peoples R China
2.Beijing Wenge Technol Co Ltd, Beijing 100080, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
4.Xinhua News Agcy, Commun Technol Bur, Beijing 100803, Peoples R China
5.State Informat Ctr, Beijing 100045, Peoples R China
6.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100049, Peoples R China
7.Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA
推荐引用方式
GB/T 7714		 Luo, Yin,Wang, Fangfang,Chen, Jun,et al. Integrating Deep Learning Approaches for Identifying News Reprint Relation[J]. IEEE ACCESS,2018,6:72163-72172.
APA Luo, Yin,Wang, Fangfang,Chen, Jun,Wang, Lei,&Zeng, Daniel Dajun.(2018).Integrating Deep Learning Approaches for Identifying News Reprint Relation.IEEE ACCESS,6,72163-72172.
MLA Luo, Yin,et al."Integrating Deep Learning Approaches for Identifying News Reprint Relation".IEEE ACCESS 6(2018):72163-72172.

入库方式： OAI收割

来源：自动化研究所

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