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Extracting Sentences of Research Originality from Full Text Academic Articles |
Chengzhi Zhang1,3(),Zheng Li2,3 |
1School of Economics & Management, Nanjing University of Science & Technology, Nanjing 210094, China 2School of Information Management, Nanjing University, Nanjing 210023, China 3Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China |
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Abstract [Objective] This paper analyzes full texts of academic articles, aiming to extract sentences of research originality as well as, exploring their characteristics. [Methods] We used full-text journal papers in the field of library, information and archives as experiment data. Then, we chose mark words, created extraction rules for sentences of research originality. Finally, we analyzed distribution of these sentences with the mark words, types, and locations. [Results] The extracted sentences were mainly divided into six categories, and most of them appeared in the top 24.8% section of each article. [Limitations] The proposed sentence extraction method needs to be optimized. [Conclusions] Sentences of research originality in the field of library, information and archives focus on concepts and theories. The categories and distributions of these sentences are various among different journals.
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Received: 14 January 2019
Published: 25 November 2019
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Corresponding Authors:
Chengzhi Zhang
E-mail: zhangcz@njust.edu.cn
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