Abstract
The large-scale biomedical semantic indexing and question-answering challenge (BioASQ) aims at the continuous advancement of methods and tools to meet the need of biomedical researchers and practitioners for efficient and precise access to the ever-increasing resources of their domain. With this purpose, during the last ten years a series of annual challenges have been organized with specific shared tasks on large-scale biomedical semantic indexing and question answering. Benchmark datasets have been concomitantly provided in alignment with the real needs of biomedical experts. BioASQ provides a unique common testbed where different teams around the world can investigate and compare new approaches for identifying and accessing biomedical knowledge. The eleventh version of the BioASQ Challenge will be held as an evaluation Lab within CLEF2023. In this version, three shared tasks will be presented: (i) the automated retrieval of relevant material for biomedical questions, and the generation of comprehensible answers. (ii) the synergistic retrieval of relevant material and generation of answers for open biomedical questions about developing topics, in collaboration with the experts posing the questions. (iii) the automated indexing of unlabelled clinical procedures-specific medical documents, primarily clinical case reports written in Spanish, with biomedical concepts and the extraction of human-interpretable evidence. As BioASQ rewards the methods that outperform the state of the art in these shared tasks, it pushes the research frontier towards approaches that accelerate access to biomedical knowledge.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
Since the introduction of BioASQ, the task on large-scale biomedical semantic indexing is called Task a, and the task on biomedical question answering is called Task b, for brevity. Despite the completion of Task a last year, we keep this naming convention for Task b, for the sake of uniformity with previous versions.
- 4.
- 5.
- 6.
- 7.
References
Cohen, K.B., Demner-Fushman, D., Ananiadou, S., Tsujii, J. (eds.): BioNLP 2017. Association for Computational Linguistics, Vancouver, Canada, August 2017. https://doi.org/10.18653/v1/W17-23, aclanthology.org/W17-2300
Donnelly, K., et al.: SNOMED-CT: the advanced terminology and coding system for eHealth. Stud. Health Technol. Inform. 121, 279 (2006)
Gasco, L., et al.: Overview of BioASQ 2021-MESINESP track. Evaluation of advance hierarchical classification techniques for scientific literature, patents and clinical trials. In: CEUR Workshop Proceedings (2021)
Kosmopoulos, A., Partalas, I., Gaussier, E., Paliouras, G., Androutsopoulos, I.: Evaluation measures for hierarchical classification: a unified view and novel approaches. Data Min. Knowl. Disc. 29(3), 820–865 (2015)
Krallinger, M., Krithara, A., Nentidis, A., Paliouras, G., Villegas, M.: BioASQ at CLEF2020: large-scale biomedical semantic indexing and question answering. In: Jose, J.M., et al. (eds.) ECIR 2020. LNCS, vol. 12036, pp. 550–556. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45442-5_71
Krithara, A., Nentidis, A., Bougiatiotis, K., Paliouras, G.: BioASQ-QA: a manually curated corpus for biomedical question answering. bioRxiv (2022)
Krithara, A., Nentidis, A., Paliouras, G., Krallinger, M., Miranda, A.: BioASQ at CLEF2021: large-scale biomedical semantic indexing and question answering. In: Hiemstra, D., Moens, M.-F., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds.) ECIR 2021. LNCS, vol. 12657, pp. 624–630. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72240-1_73
Malakasiotis, P., Pavlopoulos, I., Androutsopoulos, I., Nentidis, A.: Evaluation measures for task b. Technical report, BioASQ (2018). participants-area.bioasq.org/Tasks/b/eval_meas_2018
Ménard, P.A., Mougeot, A.: Turning silver into gold: error-focused corpus reannotation with active learning. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pp. 758–767 (2019)
Miranda-Escalada, A., et al.: Overview of DisTEMIST at BioASQ: automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources. In: Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings (2022)
Mork, J., Aronson, A., Demner-Fushman, D.: 12 years on-Is the NLM medical text indexer still useful and relevant? J. Biomed. Semant. 8(1), 8 (2017)
Mork, J., Jimeno-Yepes, A., Aronson, A.: The NLM medical text indexer system for indexing biomedical literature (2013)
Nentidis, A., et al.: Overview of BioASQ 2022: the tenth BioASQ challenge on large-scale biomedical semantic indexing and question answering. In: Experimental IR Meets Multilinguality, Multimodality, and Interaction (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS, vol. 13390, pp. 337–361. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-13643-6_22
Nentidis, A., Katsimpras, G., Vandorou, E., Krithara, A., Paliouras, G.: Overview of BioASQ tasks 10a, 10b and Synergy10 in CLEF2022. In: CEUR Workshop Proceedings, vol. 3180, pp. 171–178 (2022)
Nentidis, A., Krithara, A., Paliouras, G., Gasco, L., Krallinger, M.: BioASQ at CLEF2022: the tenth edition of the large-scale biomedical semantic indexing and question answering challenge. In: Hagen, M., et al. (eds.) ECIR 2022. LNCS, vol. 13186, pp. 429–435. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99739-7_53
Ngomo, A.C.N., Heino, N., Speck, R., Ermilov, T., Tsatsaronis, G.: Annotation tool. Project deliverable D3.3, February 2013. www.bioasq.org/sites/default/files/PublicDocuments/2013-D3.3-AnnotationTool.pdf
Packer, A.L., et al.: SciELO: uma metodologia para publicação eletrônica. Ciência da informação 27, nd-nd (1998)
Salton, G., Buckley, C.: Improving retrieval performance by relevance feedback. J. Am. Soc. Inf. Sci. 41(4), 288–297 (1990). https://doi.org/10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H
ShafieiBavani, E., Ebrahimi, M., Wong, R., Chen, F.: Summarization evaluation in the absence of human model summaries using the compositionality of word embeddings. In: Proceedings of the 27th International Conference on Computational Linguistics. pp. 905–914. Association for Computational Linguistics, Santa Fe, New Mexico, USA, August 2018. www.aclweb.org/anthology/C18-1077
Tsatsaronis, G., et al.: An overview of the BioASQ large-scale biomedical semantic indexing and question answering competition. BMC Bioinform. 16, 138 (2015). https://doi.org/10.1186/s12859-015-0564-6
Wang, L.L., et al.: CORD-19: the COVID-19 open research dataset. ArXiv (2020). arxiv.org/abs/2004.10706v2
Zavorin, I., Mork, J.G., Demner-Fushman, D.: Using learning-to-rank to enhance NLM medical text indexer results. ACL 2016, 8 (2016)
Acknowledgments
Google was a proud sponsor of the BioASQ Challenge in 2022. The eleventh edition of BioASQ is also sponsored by Atypon Systems inc. The task MedProcNER is supported by the Spanish Plan for the Advancement of Language Technologies (Plan TL), the 2020 Proyectos de I+D+i-RTI Tipo A (Descifrando El Papel De Las Profesiones En La Salud De Los Pacientes A Traves De La Mineria De Textos, PID2020-119266RA-I00). This project has received funding from the European Union Horizon Europe Coordination and Support Action under Grant Agreement No 101058779 (BIOMATDB) and DataTools4Heart - DT4H, Grant agreement No 101057849.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nentidis, A., Krithara, A., Paliouras, G., Farre-Maduell, E., Lima-Lopez, S., Krallinger, M. (2023). BioASQ at CLEF2023: The Eleventh Edition of the Large-Scale Biomedical Semantic Indexing and Question Answering Challenge. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13982. Springer, Cham. https://doi.org/10.1007/978-3-031-28241-6_66
Download citation
DOI: https://doi.org/10.1007/978-3-031-28241-6_66
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28240-9
Online ISBN: 978-3-031-28241-6
eBook Packages: Computer ScienceComputer Science (R0)