Na\"iveRole: Author-Contribution Extraction and Parsing from Biomedical Manuscripts

TL;DR

This paper introduces Na"iveRole, a novel machine learning approach for extracting structured author contribution roles from biomedical manuscripts, supported by a statistical analysis of contribution sections and achieving moderate extraction accuracy.

Contribution

It presents the first automatic method for extracting author roles from research papers, combining statistical analysis and Na"ive Bayes classification.

Findings

Na"iveRole achieves a precision of 0.68 and recall of 0.48.

Discovered common author roles using co-clustering and Open Information Extraction.

Built a training set automatically from PubMed Central contributions.

Abstract

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.