Acknowledgement Entity Recognition in CORD-19 Papers

Jian Wu, Pei Wang, Xin Wei, Sarah Rajtmajer, C. Lee Giles, Christopher Griffin


Abstract
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 F_1=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.
Anthology ID:
2020.sdp-1.3
Volume:
Proceedings of the First Workshop on Scholarly Document Processing
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | sdp
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10–19
Language:
URL:
https://aclanthology.org/2020.sdp-1.3
DOI:
10.18653/v1/2020.sdp-1.3
Bibkey:
Cite (ACL):
Jian Wu, Pei Wang, Xin Wei, Sarah Rajtmajer, C. Lee Giles, and Christopher Griffin. 2020. Acknowledgement Entity Recognition in CORD-19 Papers. In Proceedings of the First Workshop on Scholarly Document Processing, pages 10–19, Online. Association for Computational Linguistics.
Cite (Informal):
Acknowledgement Entity Recognition in CORD-19 Papers (Wu et al., sdp 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.sdp-1.3.pdf
Video:
 https://slideslive.com/38940712
Code
 lamps-lab/ackextract