A framework to automatically extract funding information from text

Subhradeep Kayal, Zubair Afzal, George Tsatsaronis, Marius Doornenbal, Sophia Katrenko, Michelle Gregory

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsGiuseppe Nicosia, Giovanni Giuffrida, Giuseppe Nicosia, Panos Pardalos, Vincenzo Sciacca, Renato Umeton
PublisherSpringer Verlag
Pages317-328
Number of pages12
ISBN (Print)9783030137083
DOIs
StatePublished - Jan 1 2019
Event4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018 - Volterra, Italy
Duration: Sep 13 2018Sep 16 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11331 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018
CountryItaly
CityVolterra
Period09/13/1809/16/18

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Cite this

Kayal, S., Afzal, Z., Tsatsaronis, G., Doornenbal, M., Katrenko, S., & Gregory, M. (2019). A framework to automatically extract funding information from text. In G. Nicosia, G. Giuffrida, G. Nicosia, P. Pardalos, V. Sciacca, & R. Umeton (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 317-328). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11331 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-13709-0_27