HarriGT: A Tool for Linking News to Science

James Ravenscroft, Amanda Clare, Maria Liakata


Abstract
Being able to reliably link scientific works to the newspaper articles that discuss them could provide a breakthrough in the way we rationalise and measure the impact of science on our society. Linking these articles is challenging because the language used in the two domains is very different, and the gathering of online resources to align the two is a substantial information retrieval endeavour. We present HarriGT, a semi-automated tool for building corpora of news articles linked to the scientific papers that they discuss. Our aim is to facilitate future development of information-retrieval tools for newspaper/scientific work citation linking. HarriGT retrieves newspaper articles from an archive containing 17 years of UK web content. It also integrates with 3 large external citation networks, leveraging named entity extraction, and document classification to surface relevant examples of scientific literature to the user. We also provide a tuned candidate ranking algorithm to highlight potential links between scientific papers and newspaper articles to the user, in order of likelihood. HarriGT is provided as an open source tool (http://harrigt.xyz).
Anthology ID:
P18-4004
Volume:
Proceedings of ACL 2018, System Demonstrations
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
19–24
Language:
URL:
https://aclanthology.org/P18-4004
DOI:
10.18653/v1/P18-4004
Bibkey:
Cite (ACL):
James Ravenscroft, Amanda Clare, and Maria Liakata. 2018. HarriGT: A Tool for Linking News to Science. In Proceedings of ACL 2018, System Demonstrations, pages 19–24, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
HarriGT: A Tool for Linking News to Science (Ravenscroft et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-4004.pdf