{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T20:03:35Z","timestamp":1773345815242,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643681849","type":"print"},{"value":"9781643681856","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T00:00:00Z","timestamp":1622073600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,27]]},"abstract":"<jats:p>Automated text classification is a natural language processing (NLP) technology that could significantly facilitate scientific literature selection. A specific topical dataset of 630 article abstracts was obtained from the PubMed database. We proposed 27 parametrized options of PubMedBERT model and 4 ensemble models to solve a binary classification task on that dataset. Three hundred tests with resamples were performed in each classification approach. The best PubMedBERT model demonstrated F1-score = 0.857 while the best ensemble model reached F1-score = 0.853. We concluded that the short scientific texts classification quality might be improved using the latest state-of-art approaches.<\/jats:p>","DOI":"10.3233\/shti210125","type":"book-chapter","created":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T12:32:48Z","timestamp":1622118768000},"source":"Crossref","is-referenced-by-count":13,"title":["The Classification of Short Scientific Texts Using Pretrained BERT Model"],"prefix":"10.3233","author":[{"given":"Gleb","family":"Danilov","sequence":"first","affiliation":[{"name":"Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation"}]},{"given":"Timur","family":"Ishankulov","sequence":"additional","affiliation":[{"name":"Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation"}]},{"given":"Konstantin","family":"Kotik","sequence":"additional","affiliation":[{"name":"Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation"}]},{"given":"Yuriy","family":"Orlov","sequence":"additional","affiliation":[{"name":"Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Moscow, Russian Federation"}]},{"given":"Mikhail","family":"Shifrin","sequence":"additional","affiliation":[{"name":"Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation"}]},{"given":"Alexander","family":"Potapov","sequence":"additional","affiliation":[{"name":"Laboratory of Biomedical Informatics and Artificial Intelligence, National Medical Research Center for Neurosurgery named after N.N. Burdenko, Moscow, Russian Federation"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Public Health and Informatics"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI210125","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T13:10:44Z","timestamp":1635167444000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI210125"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,27]]},"ISBN":["9781643681849","9781643681856"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti210125","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,27]]}}}