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

Autor:	Tkaczyk, Dominika, Collins, Andrew, Beel, Joeran
Rok vydání:	2019
Předmět:	Computer Science - Computation and Language Computer Science - Digital Libraries Computer Science - Information Retrieval Computer Science - Machine Learning Statistics - Machine Learning
Zdroj:	27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science, 2019
Druh dokumentu:	Working Paper
Popis:	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. Comment: arXiv admin note: substantial text overlap with arXiv:1802.01174
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1912.10170 Zobrazit plný text záznamu View this record from Arxiv