文本特征与科研提案产出率的关系分析
Analyzing the relationship between text features and research proposal productivity
摘要：
预测研究补助金的产出对研究资助机构、科学实体和政府机构具有相当大的相关性。在这项研究中，我们调查是否从项目标题和摘要中提取的文本特征能够识别生产性赠款。我们的分析是在三个不同的领域进行的，即医学，牙科和兽医。主题和复杂性的文本功能被用来确定生产力的预测。结果表明，文本特征与赠款生产率之间存在统计上显著的关系，但这种依赖性很弱。特征相关性分析表明，摘要文本长度和词汇多样性度量是最具鉴别力的特征。我们还发现，预测精度依赖于所考虑的项目语言，并且主题特征比文本复杂性度量更具鉴别力。我们的研究结果表明，文本特征应与其他特征相结合，以帮助识别相关的研究思路。
英文摘要：
Predicting the output of research grants is of considerable relevance to research funding bodies, scientific entities and government agencies. In this study, we investigate whether text features extracted from projects title and abstracts are able to identify productive grants. Our analysis was conducted in three distinct areas, namely Medicine, Dentistry and Veterinary Medicine. Topical and complexity text features were used to identify predictors of productivity. The results indicate that there is a statistically significant relationship between text features and grants productivity, however such a dependence is weak. A feature relevance analysis revealed that the abstract text length and metrics derived from lexical diversity are among the most discriminative features. We also found that the prediction accuracy has a dependence on the considered project language and that topical features are more discriminative than text complexity measurements. Our findings suggest that text features should be used in combination with other features to assist the identification of relevant research ideas.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
