The surge in the number of books published makes the manual evaluation methods difficult to efficiently evaluate books. The use of books' citations and alternative evaluation metrics can assist manual evaluation and reduce the cost of evaluation. However, most existing evaluation research was based on a single evaluation source with coarse-grained analysis, which may obtain incomprehensive or one-sided evaluation results of book impact. Meanwhile, relying on a single resource for book assessment may lead to the risk that the evaluation results cannot be obtained due to the lack of the evaluation data, especially for newly published books. Hence, this paper measured book impact based on an evaluation system constructed by integrating multiple evaluation sources. Specifically, we conducted finer-grained mining on the multiple evaluation sources, including books' internal evaluation resources and external evaluation resources. Various technologies (e.g. topic extraction, sentiment analysis, text classification) were used to extract corresponding evaluation metrics from the internal and external evaluation resources. Then, Expert evaluation combined with analytic hierarchy process was used to integrate the evaluation metrics and construct a book impact evaluation system. Finally, the reliability of the evaluation system was verified by comparing with the results of expert evaluation, detailed and diversified evaluation results were then obtained. The experimental results reveal that differential evaluation resources can measure the books' impacts from different dimensions, and the integration of multiple evaluation data can assess books more comprehensively. Meanwhile, the book impact evaluation system can provide personalized evaluation results according to the users' evaluation purposes. In addition, the disciplinary differences should be considered for assessing books' impacts.


翻译:出版的书籍数量激增,使得难以有效评价书籍的手工评价方法;因此,使用书籍引用和替代评价指标可以有助于人工评价并降低评价费用;然而,大多数现有的评价研究都基于单一的评价来源,有粗略的分析,可能获得图书影响的不全面或片面的评价结果;同时,依靠单一的图书评估资源可能导致由于缺乏评价数据而无法取得评价结果的风险,特别是新出版的书籍差异数据;因此,本文件根据综合多种评价来源建立的评价系统衡量书籍影响;具体地说,我们对多种评价来源进行了精细的挖掘,包括书籍的内部评价资源和外部评价资源;利用各种技术(例如专题提取、情绪分析、文本分类)从内部和外部评价资源中提取相应的评价指标;然后,利用专家评价与分析性等级进程相结合,将评价指标纳入评价指标,并建立一个书影响评价系统;最后,通过比较各种评价来源,我们进行了精细的挖掘,包括书籍内部评价资源和外部评价层面;通过将评价的结果与不同评价进行比较,可以比较,对多种评价的结果进行评估。

0
下载
关闭预览

相关内容

Integration:Integration, the VLSI Journal。 Explanation:集成,VLSI杂志。 Publisher:Elsevier。 SIT:http://dblp.uni-trier.de/db/journals/integration/
机器学习入门的经验与建议
专知会员服务
92+阅读 · 2019年10月10日
【哈佛大学商学院课程Fall 2019】机器学习可解释性
专知会员服务
103+阅读 · 2019年10月9日
已删除
将门创投
12+阅读 · 2019年7月1日
Arxiv
13+阅读 · 2021年3月3日
VIP会员
相关VIP内容
相关资讯
已删除
将门创投
12+阅读 · 2019年7月1日
Top
微信扫码咨询专知VIP会员