  Information about the contributions of individual authors to scientific
publications is important for assessing authors' achievements. Some biomedical
publications have a short section that describes authors' roles and
contributions. It is usually written in natural language and hence author
contributions cannot be trivially extracted in a machine-readable format. In
this paper, we present 1) A statistical analysis of roles in author
contributions sections, and 2) Na\"iveRole, a novel approach to extract
structured authors' roles from author contribution sections. For the first
part, we used co-clustering techniques, as well as Open Information Extraction,
to semi-automatically discover the popular roles within a corpus of 2,000
contributions sections from PubMed Central. The discovered roles were used to
automatically build a training set for Na\"iveRole, our role extractor
approach, based on Na\"ive Bayes. Na\"iveRole extracts roles with a
micro-averaged precision of 0.68, recall of 0.48 and F1 of 0.57. It is, to the
best of our knowledge, the first attempt to automatically extract author roles
from research papers. This paper is an extended version of a previous poster
published at JCDL 2018.
