Find the Funding: Entity Linking with Incomplete Funding Knowledge Bases

Gizem Aydin, Seyed Amin Tabatabaei, George Tsatsaronis, Faegheh Hasibi


Abstract
Automatic extraction of funding information from academic articles adds significant value to industry and research communities, including tracking research outcomes by funding organizations, profiling researchers and universities based on the received funding, and supporting open access policies. Two major challenges of identifying and linking funding entities are: (i) sparse graph structure of the Knowledge Base (KB), which makes the commonly used graph-based entity linking approaches suboptimal for the funding domain, (ii) missing entities in KB, which (unlike recent zero-shot approaches) requires marking entity mentions without KB entries as NIL. We propose an entity linking model that can perform NIL prediction and overcome data scarcity issues in a time and data-efficient manner. Our model builds on a transformer-based mention detection and a bi-encoder model to perform entity linking. We show that our model outperforms strong existing baselines.
Anthology ID:
2022.coling-1.168
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1937–1942
Language:
URL:
https://aclanthology.org/2022.coling-1.168
DOI:
Bibkey:
Cite (ACL):
Gizem Aydin, Seyed Amin Tabatabaei, George Tsatsaronis, and Faegheh Hasibi. 2022. Find the Funding: Entity Linking with Incomplete Funding Knowledge Bases. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1937–1942, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Find the Funding: Entity Linking with Incomplete Funding Knowledge Bases (Aydin et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.168.pdf
Code
 informagi/fund-el