  Creating scientific publications is a complex process, typically composed of
a number of different activities, such as designing the experiments, data
preparation, programming software and writing and editing the manuscript. The
information about the contributions of individual authors of a paper is
important in the context of assessing authors' scientific achievements. Some
publications in biomedical disciplines contain a description of authors' roles
in the form of a short section written in natural language, typically entitled
"Authors' contributions". In this paper, we present an analysis of roles
commonly appearing in the content of these sections, and propose an algorithm
for automatic extraction of authors' roles from natural language text in
scientific publications. During the first part of the study, we used clustering
techniques, as well as Open Information Extraction (OpenIE), to
semi-automatically discover the most popular roles within a corpus of 2,000
contributions sections obtained from PubMed Central resources. The roles
discovered by our approach include: experimenting (1,743 instances, 17% of the
entire role set within the corpus), analysis (1,343, 16%), study design (1,132,
13%), interpretation (879, 10%), conceptualization (865, 10%), paper reading
(823, 10%), paper writing (724, 8%), paper review (501, 6%), paper drafting
(351, 4%), coordination (319, 4%), data collection (76, 1%), paper review (41,
0.5%) and literature review (41, 0.5%). Discovered roles were then used to
automatically build a training set for the supervised role extractor, based on
Naive Bayes algorithm. According to the evaluation we performed, the proposed
role extraction algorithm is able to extract the roles from the text with
precision 0.71, recall 0.49 and F1 0.58.
