AffilGood: Building reliable institution name disambiguation tools to improve scientific literature analysis

AffilGood: Building reliable institution name disambiguation tools to improve scientific literature analysis. Duran-Silva, N., Accuosto, P., Przybyła, P., & Saggion, H. In Ghosal, T., Singh, A., Waard, A., Mayr, P., Naik, A., Weller, O., Lee, Y., Shen, S., & Qin, Y., editors, Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024), pages 135–144, Bangkok, Thailand, August, 2024. Association for Computational Linguistics.

Paper abstract bibtex

The accurate attribution of scientific works to research organizations is hindered by the lack of openly available manually annotated data–in particular when multilingual and complex affiliation strings are considered. The AffilGood framework introduced in this paper addresses this gap. We identify three sub-tasks relevant for institution name disambiguation and make available annotated datasets and tools aimed at each of them, including i) a dataset annotated with affiliation spans in noisy automatically-extracted strings; ii) a dataset annotated with named entities for the identification of organizations and their locations; iii) seven datasets annotated with the Research Organization Registry (ROR) identifiers for the evaluation of entity-linking systems. In addition, we describe, evaluate and make available newly developed tools that use these datasets to provide solutions for each of the identified sub-tasks. Our results confirm the value of the developed resources and methods in addressing key challenges in institution name disambiguation.

@inproceedings{duran-silva_affilgood_2024,
	address = {Bangkok, Thailand},
	title = {{AffilGood}: {Building} reliable institution name disambiguation tools to improve scientific literature analysis},
	shorttitle = {{AffilGood}},
	url = {https://aclanthology.org/2024.sdp-1.13},
	abstract = {The accurate attribution of scientific works to research organizations is hindered by the lack of openly available manually annotated data–in particular when multilingual and complex affiliation strings are considered. The AffilGood framework introduced in this paper addresses this gap. We identify three sub-tasks relevant for institution name disambiguation and make available annotated datasets and tools aimed at each of them, including i) a dataset annotated with affiliation spans in noisy automatically-extracted strings; ii) a dataset annotated with named entities for the identification of organizations and their locations; iii) seven datasets annotated with the Research Organization Registry (ROR) identifiers for the evaluation of entity-linking systems. In addition, we describe, evaluate and make available newly developed tools that use these datasets to provide solutions for each of the identified sub-tasks. Our results confirm the value of the developed resources and methods in addressing key challenges in institution name disambiguation.},
	urldate = {2024-08-14},
	booktitle = {Proceedings of the {Fourth} {Workshop} on {Scholarly} {Document} {Processing} ({SDP} 2024)},
	publisher = {Association for Computational Linguistics},
	author = {Duran-Silva, Nicolau and Accuosto, Pablo and Przybyła, Piotr and Saggion, Horacio},
	editor = {Ghosal, Tirthankar and Singh, Amanpreet and Waard, Anita and Mayr, Philipp and Naik, Aakanksha and Weller, Orion and Lee, Yoonjoo and Shen, Shannon and Qin, Yanxia},
	month = aug,
	year = {2024},
	pages = {135--144},
}

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