Projects per year
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.
|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|
|Number of pages||12|
|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
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018|
|Period||09/13/18 → 09/16/18|
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- 1 Active
Information Extraction for Chemical Reactions with University of Melbourne
Verspoor, K., Akhondi, S., Tsatsaronis, G., Drukenbrodt, C., Thorne, C., Hoessel, R., Cohn, T., Nguyen, D. Q., Zhai, Z., Fang, B., Yohsikawa, H. & Doornenbal, M.
06/1/18 → 12/31/22