@inproceedings{wu-etal-2020-acknowledgement,
title = "Acknowledgement Entity Recognition in {CORD}-19 Papers",
author = "Wu, Jian and
Wang, Pei and
Wei, Xin and
Rajtmajer, Sarah and
Giles, C. Lee and
Griffin, Christopher",
editor = "Chandrasekaran, Muthu Kumar and
de Waard, Anita and
Feigenblat, Guy and
Freitag, Dayne and
Ghosal, Tirthankar and
Hovy, Eduard and
Knoth, Petr and
Konopnicki, David and
Mayr, Philipp and
Patton, Robert M. and
Shmueli-Scheuer, Michal",
booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.sdp-1.3/",
doi = "10.18653/v1/2020.sdp-1.3",
pages = "10--19",
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 \url{https://github.com/lamps-lab/ackextract}."
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<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.</abstract>
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%0 Conference Proceedings
%T Acknowledgement Entity Recognition in CORD-19 Papers
%A Wu, Jian
%A Wang, Pei
%A Wei, Xin
%A Rajtmajer, Sarah
%A Giles, C. Lee
%A Griffin, Christopher
%Y Chandrasekaran, Muthu Kumar
%Y de Waard, Anita
%Y Feigenblat, Guy
%Y Freitag, Dayne
%Y Ghosal, Tirthankar
%Y Hovy, Eduard
%Y Knoth, Petr
%Y Konopnicki, David
%Y Mayr, Philipp
%Y Patton, Robert M.
%Y Shmueli-Scheuer, Michal
%S Proceedings of the First Workshop on Scholarly Document Processing
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F wu-etal-2020-acknowledgement
%X 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.
%R 10.18653/v1/2020.sdp-1.3
%U https://aclanthology.org/2020.sdp-1.3/
%U https://doi.org/10.18653/v1/2020.sdp-1.3
%P 10-19
Markdown (Informal)
[Acknowledgement Entity Recognition in CORD-19 Papers](https://aclanthology.org/2020.sdp-1.3/) (Wu et al., sdp 2020)
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.