Paper Info
Title
Acknowledgement Entity Recognition in CORD-19 Papers.
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.
Year
DOI
Venue
2020
10.18653/v1/2020.sdp-1.3
SDP@EMNLP
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Jian Wu100.68
Pei Wang200.34
Xin Wei300.68
Sarah Michele Rajtmajer43110.06
C. Lee Giles5111541549.48
Christopher Griffin65811.43