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Title: Acknowledgement Entity Recognition in CORD-19 Papers
Acknowledgements are ubiquitous in scholarly papers. Existing acknowledgement entity recognition methods assume all named entities are acknowledged. Here, we examine the nuances between acknowledged and named entities by analyzing sentence structure. We develop an acknowledgement extraction system, ACKEXTRACT based on open-source text mining software and evaluate our method using manually labeled data. ACKEXTRACT uses the PDF of a scholarly paper as input and outputs acknowledgement entities. Results show an overall performance of F1 = 0:92. We built a supplementary database by linking CORD-19 papers with acknowledgement entities extracted by ACKEXTRACT including persons and organizations and find that only up to 50–60% of named entities are actually acknowledged. We further analyze chronological trends of acknowledgement entities in CORD-19 papers. All codes and labeled data are publicly available at https://github.com/ lamps-lab/ackextract.  more » « less
Award ID(s):
1823288
PAR ID:
10271899
Author(s) / Creator(s):
Date Published:
Journal Name:
Proceedings of the First Workshop on Scholarly Document Processing
Page Range / eLocation ID:
11-19
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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