  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.
