nach oben

Erschienen in:

2021 | OriginalPaper | Buchkapitel

Overview of BioASQ 2021: The Ninth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering

verfasst von : Anastasios Nentidis, Georgios Katsimpras, Eirini Vandorou, Anastasia Krithara, Luis Gasco, Martin Krallinger, Georgios Paliouras

Erschienen in: Experimental IR Meets Multilinguality, Multimodality, and Interaction

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Advancing the state-of-the-art in large-scale biomedical semantic indexing and question answering is the main focus of the BioASQ challenge. BioASQ organizes respective tasks where different teams develop systems that are evaluated on the same benchmark datasets that represent the real information needs of experts in the biomedical domain. This paper presents an overview of the ninth edition of the BioASQ challenge in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2021. In this year, a new question answering task, named Synergy, is introduced to support researchers studying the COVID-19 disease and measure the ability of the participating teams to discern information while the problem is still developing. In total, 42 teams with more than 170 systems were registered to participate in the four tasks of the challenge. The evaluation results, similarly to previous years, show a performance gain against the baselines which indicates the continuous improvement of the state-of-the-art in this field.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Overview of ARQMath-2 (2021): Second CLEF Lab on Answer Retrieval for Questions on Math

Nächstes Kapitel Overview of the CLEF–2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News

https://pubmed.ncbi.nlm.nih.gov/.

https://plantl.mineco.gob.es.

https://www.ethnologue.com/guides/ethnologue200.

DeCS (Descriptores Descriptores en Ciencias de la Salud, Health Science Descriptors) is a structured controlled vocabulary created by BIREME to index scientific publications on BvSalud (Biblioteca Virtual en Salud, Virtual Health Library).

IBECS includes bibliographic references from scientific articles in health sciences published in Spanish medical journals. http://ibecs.isciii.es.

LILACS is a resource comprising scientific and technical literature from Latin America and the Caribbean countries. It includes 26 countries, 882 journals and 878,285 records, 464,451 of which are full texts https://lilacs.bvsalud.org.

Registro Español de Estudios Clínicos, a database containing summaries of clinical trials https://reec.aemps.es/reec/public/web.html.

https://cloud.google.com/blog/topics/public-datasets/google-patents-public-datasets-connecting-public-paid-and-private-patent-data.

http://participants-area.bioasq.org/results/9a/.

http://participants-area.bioasq.org/Tasks/b/eval_meas_2021/.

http://participants-area.bioasq.org/results/9b/phaseA/.

http://participants-area.bioasq.org/results/9b/phaseB/.

http://participants-area.bioasq.org/results/synergy/.

attentionxml: Label tree-based attention-aware deep model for high-performance extreme multi-label text classification

ku-dmis at bioasq 9: Data-centric and model-centric approaches for biomedical question answering

Almeida, T., Matos, S.: BIT.UA at BioASQ 8: lightweight neural document ranking with zero-shot snippet retrieval. In: CLEF (Working Notes) (2020)

Almeida, T., Matos, S.: BioASQ synergy: a strong and simple baseline rooted in relevance feedback. In: CLEF (Working Notes) (2021)

Almeida, T., Matos, S.: Universal passage weighting mechanism (UPWM) in BioASQ 9b. In: CLEF (Working Notes) (2021)

Alrowili, S., Shanker, K.: Large biomedical question answering models with ALBERT and ELECTRA. In: CLEF (Working Notes) (2021)

Alrowili, S., Shanker, V.: BioM-transformers: building large biomedical language models with BERT, ALBERT and ELECTRA. In: Proceedings of the 20th Workshop on Biomedical Language Processing, pp. 221–227. Association for Computational Linguistics, June 2021. https://www.aclweb.org/anthology/2021.bionlp-1.24

Baldwin, B., Carpenter, B.: LingPipe. World Wide Web (2003). http://alias-i.com/lingpipe

Balikas, G., et al.: Evaluation framework specifications. Project deliverable D4.1, UPMC, May 2013

10.

Campos, M., Couto, F.: Post-processing BioBERT and using voting methods for biomedical question answering. In: CLEF (Working Notes) (2021)

11.

Clark, K., Luong, M.T., Le, Q.V., Manning, C.D.: ELECTRA: pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555 (2020)

12.

Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MathSciNetMATH

13.

García-Pablos, A., Perez, N., Cuadros, M.: Vicomtech at MESINESP2: BERT-based multi-label classification models for biomedical text indexing (2021)

14.

Gasco, L., et al.: Overview of BioASQ 2021-MESINESP track. Evaluation of advance hierarchical classification techniques for scientific literature, patents and clinical trials (2021)

15.

Huang, Y., Buse, G., Abdullatif, K., Ozgur, A., Ozkirimli, E.: Pidna at BioASQ MESINESP: hybrid semantic indexing for biomedical articles in Spanish (2021)

16.

Khanna, U., Molla, D.: Transformer-based language models for factoid question answering at bioasq9b. In: CLEF (Working Notes) (2021)

17.

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)MathSciNetCrossRef

18.

Krallinger, M., et al.: Overview of the CHEMDNER patents task. In: Proceedings of the Fifth BioCreative Challenge Evaluation Workshop, pp. 63–75 (2015)

19.

Miranda-Escalada, A., Farré, E., Krallinger, M.: Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in Spanish, corpus, guidelines, methods and results. In: Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020). CEUR Workshop Proceedings (2020)

20.

Miranda-Escalada, A.: The ProfNER shared task on automatic recognition of occupation mentions in social media: systems, evaluation, guidelines, embeddings and corpora. In: Proceedings of the Sixth Social Media Mining for Health (# SMM4H) Workshop and Shared Task, pp. 13–20 (2021)

21.

Miranda-Escalada, A., Gonzalez-Agirre, A., Armengol-Estapé, J., Krallinger, M.: Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of CLEF eHealth 2020. In: Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings (2020)

22.

Molla, D., Jones, C., Nguyen, V.: Query focused multi-document summarisation of biomedical texts. arXiv preprint arXiv:2008.11986 (2020)

23.

Molla, D., Khanna, U., Galat, D., Nguyen, V., Rybinski, M.: Query-focused extractive summarisation for finding ideal answers to biomedical and COVID-19 questions. In: CLEF (Working Notes) (2021)

24.

Mork, J.G., Demner-Fushman, D., Schmidt, S.C., Aronson, A.R.: Recent enhancements to the NLM medical text indexer. In: Proceedings of Question Answering Lab at CLEF (2014)

25.

Nentidis, A., et al.: Overview of BioASQ 2020: the eighth BioASQ challenge on large-scale biomedical semantic indexing and question answering. In: Arampatzis, A., et al. (eds.) CLEF 2020. LNCS, vol. 12260, pp. 194–214. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58219-7_16CrossRef

26.

Ozyurt, I.B.: On the effectiveness of small, discriminatively pre-trained language representation models for biomedical text mining. In: Proceedings of the First Workshop on Scholarly Document Processing, pp. 104–112 (2020)

27.

Ozyurt, I.B.: End-to-end biomedical question answering via bio-answerfinder and discriminative language representation models. In: CLEF (Working Notes) (2021)

28.

Ozyurt, I.B., Bandrowski, A., Grethe, J.S.: Bio-AnswerFinder: a system to find answers to questions from biomedical texts. Database 2020 (2020)

29.

Pappas, D., Stavropoulos, P., Androutsopoulos, I.: AUEB-NLP at BioASQ 8: biomedical document and snippet retrieval (2020)

30.

Peng, S., You, R., Wang, H., Zhai, C., Mamitsuka, H., Zhu, S.: DeepMesh: deep semantic representation for improving large-scale mesh indexing. Bioinformatics 32(12), i70–i79 (2016)CrossRef

31.

Rae, A., Mork, J., Demner-Fushman, D.: A neural text ranking approach for automatic mesh indexing. In: CLEF (Working Notes) (2021)

32.

Rae, A.R., Pritchard, D.O., Mork, J.G., Demner-Fushman, D.: Automatic mesh indexing: revisiting the subheading attachment problem. In: AMIA Annual Symposium Proceedings, vol. 2020, p. 1031. American Medical Informatics Association (2020)

33.

Raffel, C.: Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019)

34.

Ribadas, F.J., De Campos, L.M., Darriba, V.M., Romero, A.E.: CoLe and UTAI at BioASQ 2015: experiments with similarity based descriptor assignment. CEUR Workshop Proc. 1391 (2015)

35.

Rodriguez-Penagos, C.: Overview of MESINESP8, a Spanish medical semantic indexing task within BioASQ 2020 (2020)

36.

Ruas, P., Andrade, V.D.T., Couto, F.M.: LASIGE-BioTM at MESINESP2: entity linking with semantic similarity and extreme multi-label classification on Spanish biomedical documents (2021)

37.

Sarrouti, M., Gupta, D., Abacha, A.B., Demner-Fushman, D.: NLM at BioASQ 2021: deep learning-based methods for biomedical question answering about COVID-19. In: CLEF (Working Notes) (2021)

38.

Torres-Salinas, D., Robinson-Garcia, N., van Schalkwyk, F., Nane, G.F., Castillo-Valdivieso, P.: The growth of COVID-19 scientific literature: a forecast analysis of different daily time series in specific settings. arXiv preprint arXiv:2101.12455 (2021)

39.

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/s12859-015-0564-6CrossRef

40.

Tsoumakas, G., Laliotis, M., Markontanatos, N., Vlahavas, I.: Large-scale semantic indexing of biomedical publications. In: 1st BioASQ Workshop: A Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering (2013)

41.

Wang, L.L., et al.: CORD-19: the COVID-19 open research dataset. ArXiv (2020)

42.

Wei, C.H., Leaman, R., Lu, Z.: Beyond accuracy: creating interoperable and scalable text-mining web services. Bioinform. (Oxford, Engl.) 32(12), 1907–10 (2016). https://doi.org/10.1093/bioinformatics/btv760CrossRef

43.

Xue, L., et al.: mT5: a massively multilingual pre-trained text-to-text transformer. arXiv preprint arXiv:2010.11934 (2020)

44.

Yang, Z., Zhou, Y., Eric, N.: Learning to answer biomedical questions: OAQA at BioASQ 4b. ACL 2016, 23 (2016)

45.

Yoon, W., Jackson, R., Kang, J., Lagerberg, A.: Sequence tagging for biomedical extractive question answering. arXiv preprint arXiv:2104.07535 (2021)

46.

You, R., Liu, Y., Mamitsuka, H., Zhu, S.: BERTMeSH: deep contextual representation learning for large-scale high-performance MeSH indexing with full text. Bioinformatics 37(5), 684–692 (2021)CrossRef

47.

Zavorin, I., Mork, J.G., Demner-Fushman, D.: Using learning-to-rank to enhance NLM medical text indexer results. ACL 2016, 8 (2016)

48.

Zhang, Y., Han, J.C., Tsai, R.T.H.: NCU-IISR/AS-GIS: results of various pre-trained biomedical language models and logistic regression model in BioASQ task 9b phase b. In: CLEF (Working Notes) (2021)

Titel: Overview of BioASQ 2021: The Ninth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
verfasst von: Anastasios Nentidis
Georgios Katsimpras
Eirini Vandorou
Anastasia Krithara
Luis Gasco
Martin Krallinger
Georgios Paliouras
Verlag: Springer International Publishing
Buch: Experimental IR Meets Multilinguality, Multimodality, and Interaction
Print ISBN: 978-3-030-85250-4

Electronic ISBN: 978-3-030-85251-1

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-85251-1_18

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner