Abstract
Combination therapies have become the standard of care for diseases such as cancer, tuberculosis, malaria and HIV. However, the combinatorial set of available multi-drug treatments creates a challenge in identifying effective combination therapies available in a situation. To assist medical professionals in identifying beneficial drug-combinations, we construct an expert-annotated dataset for extracting information about the efficacy of drug combinations from the scientific literature. Beyond its practical utility, the dataset also presents a unique NLP challenge, as the first relation extraction dataset consisting of variable-length relations. Furthermore, the relations in this dataset predominantly require language understanding beyond the sentence level, adding to the challenge of this task. We provide a promising baseline model and identify clear areas for further improvement. We release our dataset, code, and baseline models publicly to encourage the NLP community to participate in this task.
| Original language | English |
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| Title of host publication | NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics |
| Subtitle of host publication | Human Language Technologies, Proceedings of the Conference |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 3190-3203 |
| Number of pages | 14 |
| ISBN (Electronic) | 9781955917711 |
| State | Published - 2022 |
| Event | 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022 - Seattle, United States Duration: 10 Jul 2022 → 15 Jul 2022 |
Publication series
| Name | NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference |
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Conference
| Conference | 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022 |
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| Country/Territory | United States |
| City | Seattle |
| Period | 10/07/22 → 15/07/22 |
Bibliographical note
Funding Information:This project has received funding in part from the European Research Council (ERC) under the European Union's Horizon2020 research and innovation programme, grant agreement 802774 (iEXTRACT), and in part from the NSF Convergence Accelerator Award #2132318. We would also like to thank our annotators from the Shamay lab at the Faculty of Biomedical Engineering, Technion, including Shaked Launer-Wachs, Yuval Harris, Maytal Avrashami, Hagit Sason-Bauer and Yakir Amrusi
Funding Information:
This project has received funding in part from the European Research Council (ERC) under the European Union’s Horizon2020 research and innovation programme, grant agreement 802774 (iEX-TRACT), and in part from the NSF Convergence Accelerator Award #2132318. We would also like to thank our annotators from the Shamay lab at the Faculty of Biomedical Engineering, Technion, including Shaked Launer-Wachs, Yuval Harris, May-tal Avrashami, Hagit Sason-Bauer and Yakir Am-rusi
Publisher Copyright:
© 2022 Association for Computational Linguistics.
