论文标题
对网络科学的确认文本的全面分析：关于四个科学领域的案例研究
A Comprehensive Analysis of Acknowledgement Texts in Web of Science: a case study on four scientific domains
论文作者
论文摘要
对认可的分析特别有趣，因为承认不仅可以提供有关资金的信息，而且还可以揭示对作者身份和研究人员的协作模式的隐藏贡献，进行了研究的背景以及学术工作的具体方面。本研究的重点是分析科学网络（WOS）核心收集中索引的大量确认文本样本。从2014年至2019年发表在一本英语的科学期刊上，来自四个不同科学领域的记录类型的“文章”和“评论”，即社会科学，经济学，海洋学和计算机科学。使用指定的实体识别（NER）标签者从确认文本中提取了六种类型的公认实体，即资助机构，赠款编号，个人，大学，公司和杂项，并随后进行了检查。对确认文本的一般分析表明，WOS中资金信息的索引是不完整的。对自动提取的实体的分析揭示了不同科学领域之间不同类型的公认实体分布的差异和不同的模式。在公认的实体与科学领域与公认的实体和实体类型之间发现了牢固的关联。仅发现引用数量和公认实体数量之间的相关性可忽略不计。通常，确认文本中的单词数与公认的资助组织，大学，个人和其他实体的数量正相关。同时，确认句子数量较大的文本具有更多公认的个人和其他类别。
Analysis of acknowledgments is particularly interesting as acknowledgments may give information not only about funding, but they are also able to reveal hidden contributions to authorship and the researcher's collaboration patterns, context in which research was conducted, and specific aspects of the academic work. The focus of the present research is the analysis of a large sample of acknowledgement texts indexed in the Web of Science (WoS) Core Collection. Record types 'article' and 'review' from four different scientific domains, namely social sciences, economics, oceanography and computer science, published from 2014 to 2019 in a scientific journal in English were considered. Six types of acknowledged entities, i.e., funding agency, grant number, individuals, university, corporation and miscellaneous, were extracted from the acknowledgement texts using a Named Entity Recognition (NER) tagger and subsequently examined. A general analysis of the acknowledgement texts showed that indexing of funding information in WoS is incomplete. The analysis of the automatically extracted entities revealed differences and distinct patterns in the distribution of acknowledged entities of different types between different scientific domains. A strong association was found between acknowledged entity and scientific domain and acknowledged entity and entity type. Only negligible correlation was found between the number of citations and the number of acknowledged entities. Generally, the number of words in the acknowledgement texts positively correlates with the number of acknowledged funding organizations, universities, individuals and miscellaneous entities. At the same time, acknowledgement texts with the larger number of sentences have more acknowledged individuals and miscellaneous categories.
