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.

预测研究赠款的产出与研究资助机构，科学实体和政府机构有很大的关系。在这项研究中，我们调查了从项目标题和摘要中提取的文本特征是否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，分别是医学，牙科和兽医学。主题和复杂性文本功能用于识别生产力的预测指标。结果表明，文本特征与授予生产力之间存在统计上显着的关系，但是这种依赖性很弱。特征相关性分析表明，抽象文本的长度和从词汇多样性中得出的度量标准是最有区别的特征之一。我们还发现，预测准确性与所考虑的项目语言有关，并且主题特征比文本复杂性度量更具判别力。我们的发现表明，文本功能应与其他功能结合使用，以帮助识别相关的研究思路。