Extracting Funder Information from Scientific Papers - Experiences with Question Answering

Abstract

This paper is about automatic recognition of entities that funded a research work in economics as being expressed in a publication. While many works apply rules and/or regular expressions to candidate sections within the text, we follow a question answering (QA) based approach to identify those passages that are most likely to inform us about funding. With regard to a digital library scenario, we are dealing with three more challenges: confirming that our approach at least outperforms manual indexing, disambiguation of funding organizations by linking their names to authority data, and integrating the generated metadata into a digital library application. Our computational results by means of machine learning techniques show that our QA performs similar to a previous work (AckNER), although we operated on rather small sets of training and test data. While manual indexing is still needed for a gold standard of reliable metadata, the identification of funding entities only worked for a subset of funder names.