A framework to automatically extract funding information from text

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

doi:10.1007/978-3-030-13709-0_27

A framework to automatically extract funding information from text

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

Elsevier Labs

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

3 Scopus citations

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 language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Giuseppe Nicosia, Giovanni Giuffrida, Giuseppe Nicosia, Panos Pardalos, Vincenzo Sciacca, Renato Umeton
Publisher	Springer Verlag
Pages	317-328
Number of pages	12
ISBN (Print)	9783030137083
DOIs	https://doi.org/10.1007/978-3-030-13709-0_27
State	Published - Jan 1 2019
Event	4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018 - Volterra, Italy Duration: Sep 13 2018 → Sep 16 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11331 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018
Country	Italy
City	Volterra
Period	09/13/18 → 09/16/18

Access to Document

10.1007/978-3-030-13709-0_27

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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

Kayal, Subhradeep ; Afzal, Zubair ; Tsatsaronis, George ; Doornenbal, Marius ; Katrenko, Sophia ; Gregory, Michelle. / A framework to automatically extract funding information from text. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Giuseppe Nicosia ; Giovanni Giuffrida ; Giuseppe Nicosia ; Panos Pardalos ; Vincenzo Sciacca ; Renato Umeton. Springer Verlag, 2019. pp. 317-328 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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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). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11331 LNCS, Springer Verlag, pp. 317-328, 4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018, Volterra, Italy, 09/13/18. https://doi.org/10.1007/978-3-030-13709-0_27

A framework to automatically extract funding information from text. / Kayal, Subhradeep; Afzal, Zubair; Tsatsaronis, George ; Doornenbal, Marius; Katrenko, Sophia; Gregory, Michelle.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Giuseppe Nicosia; Giovanni Giuffrida; Giuseppe Nicosia; Panos Pardalos; Vincenzo Sciacca; Renato Umeton. Springer Verlag, 2019. p. 317-328 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11331 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AU - Gregory, Michelle

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AB - 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.

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ER -

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