@inbook{c6066553e45e4a9eabccbe57cec57172,
title = "A framework to automatically extract funding information from text",
abstract = "Many would argue that the currency of research is citations; however, researchers and funding organizations alike are lacking tools with which they can explore how this currency translates to funding opportunities. Motivated by this need, in this paper we address one of the fundamental problems facing the development of such a tool, namely the problem of automatically extracting funding information from scientific articles. For this purpose, we experiment with a two-stage framework which ingests text, filters paragraphs which contain funding information, and then combines sequential learning methods to detect named entities in a novel ensemble approach. We present a comparative analysis of each independent component of this pipeline, named FundingFinder, the results of which indicate that the said pipeline can extract the funding organizations and the associated grants, from scientific articles, accurately and efficiently.",
author = "Subhradeep Kayal and Zubair Afzal and George Tsatsaronis and Marius Doornenbal and Sophia Katrenko and Michelle Gregory",
year = "2019",
month = jan
day = "1",
doi = "10.1007/978-3-030-13709-0_27",
language = "English",
isbn = "9783030137083",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "317--328",
editor = "Giuseppe Nicosia and Giovanni Giuffrida and Giuseppe Nicosia and Panos Pardalos and Vincenzo Sciacca and Renato Umeton",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
note = "null ; Conference date: 13-09-2018 Through 16-09-2018",
}