{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T16:56:41Z","timestamp":1781974601594,"version":"3.54.5"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031136429","type":"print"},{"value":"9783031136436","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-13643-6_22","type":"book-chapter","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T15:03:19Z","timestamp":1661353399000},"page":"337-361","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Overview of\u00a0BioASQ 2022: The Tenth BioASQ Challenge on\u00a0Large-Scale Biomedical Semantic Indexing and\u00a0Question Answering"],"prefix":"10.1007","author":[{"given":"Anastasios","family":"Nentidis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgios","family":"Katsimpras","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eirini","family":"Vandorou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anastasia","family":"Krithara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Miranda-Escalada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis","family":"Gasco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Krallinger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgios","family":"Paliouras","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,8,25]]},"reference":[{"key":"22_CR1","unstructured":"Almeida, T., Matos, S.: BioASQ synergy: a strong and simple baseline rooted in relevance feedback. CLEF (Working Notes) (2021)"},{"key":"22_CR2","unstructured":"Almeida, T., Matos, S.: Universal passage weighting mechanism (UPWM) in BioASQ 9b. CLEF (Working Notes) (2021)"},{"key":"22_CR3","doi-asserted-by":"crossref","unstructured":"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\u2013227. Association for Computational Linguistics, June 2021. https:\/\/www.aclweb.org\/anthology\/2021.bionlp-1.24","DOI":"10.18653\/v1\/2021.bionlp-1.24"},{"issue":"10","key":"22_CR4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pbio.3001296","volume":"19","author":"T Amano","year":"2021","unstructured":"Amano, T., et al.: Tapping into non-English-language science for the conservation of global biodiversity. PLoS Biol. 19(10), e3001296 (2021)","journal-title":"PLoS Biol."},{"key":"22_CR5","unstructured":"Baldwin, B., Carpenter, B.: Lingpipe (2003). World Wide Web: http:\/\/alias-i.com\/lingpipe"},{"key":"22_CR6","unstructured":"Balikas, G., et al.: Evaluation framework specifications. Project deliverable D4.1, UPMC, May 2013"},{"key":"22_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-2801-4","volume-title":"Principles of Health Interoperability HL7 and SNOMED","author":"T Benson","year":"2012","unstructured":"Benson, T.: Principles of Health Interoperability HL7 and SNOMED. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-1-4471-2801-4"},{"key":"22_CR8","unstructured":"Bernik, M., Tovornik, R., Fabjan, B., Marco-Ruiz, L.: Diag\u00f1oza: a natural language processing tool for automatic annotation of clinical free text with SNOMED-CT (2022)"},{"key":"22_CR9","unstructured":"Borchert, F., Schapranow, M.P.: Hpi-dhc @ bioasq distemist: Spanish biomedical entity linking with cross-lingual candidate retrieval and rule-based reranking (2022)"},{"key":"22_CR10","unstructured":"Castano, J., Gambarte, M.L., Otero, C., Luna, D.: A simple terminology-based approach to clinical entity recognition (2022)"},{"key":"22_CR11","unstructured":"Chizhikova, M., Collado-Monta\u00f1ez, J., L\u00f3pez-\u00dabeda, P., D\u00edaz-Galiano, M.C., Ure\u00f1a-L\u00f3pez, L.A., Mart\u00edn-Valdivia, M.T.: SINAI at CLEF 2022: Leveraging biomedical transformers to detect and normalize disease mentions (2022)"},{"key":"22_CR12","unstructured":"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)"},{"key":"22_CR13","first-page":"1","volume":"7","author":"J Demsar","year":"2006","unstructured":"Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Gonzalez-Agirre, A., Marimon, M., Intxaurrondo, A., Rabal, O., Villegas, M., Krallinger, M.: Pharmaconer: pharmacological substances, compounds and proteins named entity recognition track. In: Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, pp. 1\u201310 (2019)","DOI":"10.18653\/v1\/D19-5701"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Islamaj Dogan, R., Murray, G.C., N\u00e9v\u00e9ol, A., Lu, Z.: Understanding pubmed\u00ae user search behavior through log analysis. Database 2009 (2009)","DOI":"10.1093\/database\/bap018"},{"key":"22_CR16","doi-asserted-by":"publisher","unstructured":"Islamaj\u00a0Do\u011fan, R., Leaman, R., Lu, Z.: NCBI disease corpus: a resource for disease name recognition and concept normalization. J. Biomed. Informa. 47, 1\u201310 (2014). https:\/\/doi.org\/10.1016\/j.jbi.2013.12.006. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1532046413001974","DOI":"10.1016\/j.jbi.2013.12.006"},{"issue":"3","key":"22_CR17","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1007\/s10618-014-0382-x","volume":"29","author":"A Kosmopoulos","year":"2014","unstructured":"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\u2013865 (2014). https:\/\/doi.org\/10.1007\/s10618-014-0382-x","journal-title":"Data Min. Knowl. Disc."},{"key":"22_CR18","unstructured":"Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R.: Albert: a lite BERT for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942 (2019)"},{"key":"22_CR19","unstructured":"Li, C., Yates, A., MacAvaney, S., He, B., Sun, Y.: Parade: passage representation aggregation for document reranking. arXiv preprint arXiv:2008.09093 (2020)"},{"key":"22_CR20","unstructured":"Miranda-Escalada, A., Farr\u00e9, 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)"},{"key":"22_CR21","unstructured":"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 (2022)"},{"key":"22_CR22","unstructured":"Miranda-Escalada, A., Gonzalez-Agirre, A., Armengol-Estap\u00e9, 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)"},{"key":"22_CR23","unstructured":"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)"},{"key":"22_CR24","unstructured":"Moscato, V., Postiglione, M., Sperl[\u00ed], G.: Biomedical Spanish language models for entity recognition and linking at BioASQ DisTEMIST (2022)"},{"key":"22_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/978-3-030-85251-1_18","volume-title":"Experimental IR Meets Multilinguality, Multimodality, and Interaction","author":"A Nentidis","year":"2021","unstructured":"Nentidis, A., et al.: Overview of BioASQ 2021: the ninth BioASQ challenge on large-scale biomedical semantic indexing and\u00a0question answering. In: Candan, K.S., et al. (eds.) CLEF 2021. LNCS, vol. 12880, pp. 239\u2013263. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-85251-1_18"},{"key":"22_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1007\/978-3-030-58219-7_16","volume-title":"Experimental IR Meets Multilinguality, Multimodality, and Interaction","author":"A Nentidis","year":"2020","unstructured":"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\u2013214. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58219-7_16"},{"key":"22_CR27","unstructured":"Neves, A.: Unicage at distemist - named entity recognition system using only bash and unicage tools (2022)"},{"key":"22_CR28","unstructured":"Ozyurt, I.B.: End-to-end biomedical question answering via bio-answerfinder and discriminative language representation models. CLEF (Working Notes) (2021)"},{"key":"22_CR29","unstructured":"Rae, A.R., Mork, J.G., Demner-Fushman, D.: A neural text ranking approach for automatic mesh indexing. In: CLEF (Working Notes), pp. 302\u2013312 (2021)"},{"key":"22_CR30","unstructured":"Reyes-Aguill\u00f3n, J., del Moral, R., Ramos-Flores, O., G\u00f3mez-Adorno, H., Bel-Enguix, G.: Clinical named entity recognition and linking using BERT in combination with Spanish medical embeddings (2022)"},{"key":"22_CR31","unstructured":"Tamayo, A., Burgos, D.A., Gelbukh, A.: mBERT and simple post-processing: a baseline for disease mention detection in Spanish (2022)"},{"key":"22_CR32","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1186\/s12859-015-0564-6","volume":"16","author":"G Tsatsaronis","year":"2015","unstructured":"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","journal-title":"BMC Bioinform."},{"key":"22_CR33","unstructured":"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)"},{"key":"22_CR34","doi-asserted-by":"publisher","unstructured":"Uzuner, O., South, B.R., Shen, S., DuVall, S.L.: 2010 i2b2\/VA challenge on concepts, assertions, and relations in clinical text. J. Am. Med. Inform. Assoc. 18(5), 552\u2013556 (2011). https:\/\/doi.org\/10.1136\/amiajnl-2011-000203","DOI":"10.1136\/amiajnl-2011-000203"},{"key":"22_CR35","unstructured":"Wang, L.L., et al.: CORD-19: the COVID-19 open research dataset. ArXiv (2020)"},{"issue":"12","key":"22_CR36","doi-asserted-by":"publisher","first-page":"1907","DOI":"10.1093\/bioinformatics\/btv760","volume":"32","author":"CH Wei","year":"2016","unstructured":"Wei, C.H., Leaman, R., Lu, Z.: Beyond accuracy: creating interoperable and scalable text-mining web services. Bioinform. (Oxford, Engl.) 32(12), 1907\u201310 (2016). https:\/\/doi.org\/10.1093\/bioinformatics\/btv760","journal-title":"Bioinform. (Oxford, Engl.)"},{"key":"22_CR37","first-page":"23","volume":"2016","author":"Z Yang","year":"2016","unstructured":"Yang, Z., Zhou, Y., Eric, N.: Learning to answer biomedical questions: Oaqa at bioasq 4b. ACL 2016, 23 (2016)","journal-title":"ACL"},{"key":"22_CR38","first-page":"8","volume":"2016","author":"I Zavorin","year":"2016","unstructured":"Zavorin, I., Mork, J.G., Demner-Fushman, D.: Using learning-to-rank to enhance NLM medical text indexer results. ACL 2016, 8 (2016)","journal-title":"ACL"},{"key":"22_CR39","unstructured":"Zhang, Y., Han, J.C., Tsai, R.T.H.: NCU-IISR\/AS-GIS: results of various pre-trained biomedical language models and linear regression model in BioASQ task 9b phase b. In: CEUR Workshop Proceedings (2021)"}],"container-title":["Lecture Notes in Computer Science","Experimental IR Meets Multilinguality, Multimodality, and Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-13643-6_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:36:26Z","timestamp":1710261386000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-13643-6_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031136429","9783031136436"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-13643-6_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"25 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CLEF","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference of the Cross-Language Evaluation Forum for European Languages","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bologna","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"clef2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/clef2022.clef-initiative.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"50% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7 best of labs + 14 lab overviews","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}