{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T03:32:18Z","timestamp":1768793538506,"version":"3.49.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s00521-021-06053-z","type":"journal-article","created":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T07:03:59Z","timestamp":1620716639000},"page":"937-950","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Thesaurus-based word embeddings for automated biomedical literature classification"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4847-0100","authenticated-orcid":false,"given":"Dimitrios A.","family":"Koutsomitropoulos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas D.","family":"Andriopoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,11]]},"reference":[{"key":"6053_CR1","volume-title":"Introduction to information retrieval","author":"DM Christopher","year":"2008","unstructured":"Christopher DM, Prabhakar R, Hinrich S (2008) Introduction to information retrieval. Cambridge University Press, Cambridge"},{"issue":"5","key":"6053_CR2","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1093\/bioinformatics\/btz756","volume":"36","author":"S Dai","year":"2020","unstructured":"Dai S, You R, Lu Z, Huang X, Mamitsuka H, Zhu S (2020) FullMeSH: improving large-scale MeSH indexing with full text. Bioinformatics (Oxford, England) 36(5):1533\u20131541. https:\/\/doi.org\/10.1093\/bioinformatics\/btz756","journal-title":"Bioinformatics (Oxford, England)"},{"key":"6053_CR3","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT 2019, pages 4171\u20134186 Minneapolis, Minnesota, June 2\u2013June 7"},{"key":"6053_CR4","unstructured":"Europe PMC Consortium. Metadata of all Full-Text Europe PMC articles. [Online]. Available: https:\/\/europepmc.org\/ftp\/pmclitemetadata\/"},{"key":"6053_CR5","volume-title":"A synopsis of linguistic theory 1930\u201355, 1952\u201359, 1\u201332","author":"JR Firth","year":"1957","unstructured":"Firth JR (1957) A synopsis of linguistic theory 1930\u201355, 1952\u201359, 1\u201332. Blackwell, Oxford"},{"key":"6053_CR6","unstructured":"General Language Understanding Evaluation. GLUE [Online]. https:\/\/gluebenchmark.com"},{"key":"6053_CR7","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. The MIT Press, Cambridge"},{"key":"6053_CR8","unstructured":"Hedtke, J., Petrov, S (2019) Semantic similarity search. Machine Learning\u2014Projects Fall 2019\u2013CS229, cs229.stanford.edu"},{"key":"6053_CR9","doi-asserted-by":"crossref","unstructured":"Jin Q, Dhingra B, Cohen W. W, Lu X (2019) Probing Biomedical embeddings from language models. arXiv:1904.02181 [cs.CL], NAACL","DOI":"10.18653\/v1\/W19-2011"},{"key":"6053_CR10","doi-asserted-by":"crossref","unstructured":"Joulin A, Grave E, Bojanowski P, Mikolov T (2016) Bag of tricks for efficient text classification. arXiv:1607.01759v3 [cs.CL]","DOI":"10.18653\/v1\/E17-2068"},{"key":"6053_CR11","doi-asserted-by":"crossref","unstructured":"Koutsomitropoulos D, Andriopoulos A (2020) Automated MeSH indexing of biomedical literature using contextualized word representations. In: Proc. of the 16th Int. Conference on Artificial Intelligence Applications and Innovations (AIAI), IFIP AICT vol. 583, pp. 343\u2013354. Springer 2020","DOI":"10.1007\/978-3-030-49161-1_29"},{"key":"6053_CR12","doi-asserted-by":"crossref","unstructured":"Koutsomitropoulos D, Andriopoulos A, Likothanassis S (2019) Subject classification of learning resources using Word embeddings and semantic thesauri. In: IEEE Innovations in Intelligent Systems and Applications (INISTA), Sofia, Bulgaria","DOI":"10.1109\/INISTA.2019.8778377"},{"issue":"5","key":"6053_CR13","first-page":"95","volume":"20","author":"D Koutsomitropoulos","year":"2020","unstructured":"Koutsomitropoulos D, Andriopoulos A, Likothanassis S (2020) Semantic classification and indexing of open educational resources with word embeddings and ontologies. Cybern Inf Technol 20(5):95\u2013116","journal-title":"Cybern Inf Technol"},{"key":"6053_CR14","unstructured":"Le QV, Mikolov T (2014) Distributed representations of sentences and documents. In: 31st International Conference on Machine Learning, ICML, Beijing, China"},{"key":"6053_CR15","volume-title":"Word embedding for understanding natural language: a survey","author":"Y Li","year":"2017","unstructured":"Li Y, Yang T (2017) Word embedding for understanding natural language: a survey. Springer, NewYork"},{"key":"6053_CR16","doi-asserted-by":"publisher","unstructured":"Lipton Z, Elkan C, & Naryanaswamy, B (2014) Optimal Thresholding of classifiers to maximize F1 measure. Machine learning and knowledge discovery in databases. In: European Conference, ECML PKDD proceedings. ECML PKDD (Conference). 8725. https:\/\/doi.org\/10.1007\/978-3-662-44851-9_15.","DOI":"10.1007\/978-3-662-44851-9_15"},{"issue":"1","key":"6053_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13326-017-0123-3","volume":"8","author":"Y Mao","year":"2017","unstructured":"Mao Y, Lu Z (2017) MeSH now: automatic MeSH indexing at PubMed scale via learning to rank. J Biomed Semant 8(1):1\u20139. https:\/\/doi.org\/10.1186\/s13326-017-0123-3","journal-title":"J Biomed Semant"},{"key":"6053_CR18","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. In: ICLR Workshop"},{"key":"6053_CR19","unstructured":"Mork JG, Jimeno-Yepes A, Aronson, AR (2013) The NLM medical text indexer system for indexing biomedical literature. In: Conference and Labs of the Evaluation Forum 2013 (CLEF 2013), Valencia, Spain"},{"issue":"12","key":"6053_CR20","doi-asserted-by":"publisher","first-page":"i70","DOI":"10.1093\/bioinformatics\/btw294","volume":"32","author":"S Peng","year":"2016","unstructured":"Peng S, You R, Wang H, Zhai C, Mamitsuka H, Zhu S (2016) DeepMeSH: deep semantic representation for improving large-scale MeSH indexing. Bioinformatics 32(12):i70\u2013i79. https:\/\/doi.org\/10.1093\/bioinformatics\/btw294","journal-title":"Bioinformatics"},{"key":"6053_CR21","doi-asserted-by":"crossref","unstructured":"Peng Y, Yan S, Lu Z (2019) Transfer learning in biomedical natural language processing: an evaluation of BERT and ELMo on ten benchmarking datasets. In: Proceedings of the Workshop on Biomedical Natural Language Processing (BioNLP).","DOI":"10.18653\/v1\/W19-5006"},{"key":"6053_CR22","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD (2014) GloVe: global vectors for word representation. In: Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532-1543, Doha, Qatar","DOI":"10.3115\/v1\/D14-1162"},{"key":"6053_CR23","doi-asserted-by":"crossref","unstructured":"Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. arXiv:1802.05365v2 [cs.CL], NAACL","DOI":"10.18653\/v1\/N18-1202"},{"issue":"8","key":"6053_CR24","first-page":"9","volume":"1","author":"A Radford","year":"2019","unstructured":"Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I (2019) Language models are unsupervised multitask learners. OpenAI Blog 1(8):9","journal-title":"OpenAI Blog"},{"issue":"4","key":"6053_CR25","doi-asserted-by":"publisher","first-page":"e48","DOI":"10.2196\/medinform.7059","volume":"5","author":"B Segura","year":"2017","unstructured":"Segura B, Mart\u00ednez P, Carruan MA (2017) Search and graph database technologies for biomedical semantic indexing: experimental analysis. JMIR Med Inform 5(4):e48. https:\/\/doi.org\/10.2196\/medinform.7059","journal-title":"JMIR Med Inform"},{"key":"6053_CR26","unstructured":"U.S. Department of Health & Human Services, MEDLINE\u00aePubMed\u00ae XML Element Descriptions and their Attributes, 2018. [Online]. Available: https:\/\/www.nlm.nih.gov\/bsd\/licensee\/elements_descriptions.html"},{"key":"6053_CR27","unstructured":"U.S. National Library of Medicine. ClinicalTrials.gov [Online]. Available: https:\/\/clinicaltrials.gov"},{"key":"6053_CR28","unstructured":"U.S. National Library of Medicine. Medical Subject Headings, 2019. [Online]. Available: https:\/\/www.nlm.nih.gov\/mesh\/meshhome.html"},{"key":"6053_CR29","unstructured":"U.S. National Library of Medicine. PubMed.gov [Online]. https:\/\/www.nlm.nih.gov\/databases\/download\/pubmed_medline.html"},{"key":"6053_CR30","doi-asserted-by":"crossref","unstructured":"Van Assem M, Malais\u00e9 V, Miles A, Schreiber G (2006) A method to convert thesauri to SKOS. In: The Semantic Web: Research and Applications: 3rd European Semantic Web Conference, ESWC, Proceedings (Vol. 4011, p. 95), Springer, Budva, Montenegro","DOI":"10.1007\/11762256_10"},{"key":"6053_CR31","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 6000\u20136010"},{"issue":"19","key":"6053_CR32","doi-asserted-by":"publisher","first-page":"3794","DOI":"10.1093\/bioinformatics\/btz142","volume":"35","author":"G Xun","year":"2019","unstructured":"Xun G, Jha K, Yuan Y, Wang Y, Zhang A (2019) MeSHProbeNet: a self-attentive probe net for MeSH indexing. Bioinformatics 35(19):3794\u20133802. https:\/\/doi.org\/10.1093\/bioinformatics\/btz142","journal-title":"Bioinformatics"},{"key":"6053_CR33","doi-asserted-by":"publisher","unstructured":"You R, & Liu Y, Mamitsuka \u0396hu S (2020) BERTMeSH: Deep contextual representation learning for large-scale high-performance MeSH indexing with full text. DOI: https:\/\/doi.org\/10.1101\/2020.07.04.187674","DOI":"10.1101\/2020.07.04.187674"},{"key":"6053_CR34","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1038\/s41597-019-0055-0","volume":"6","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Chen Q, Yang Z et al (2019) BioWordVec, improving biomedical word embeddings with subword information and MeSH. Sci Data 6:52. https:\/\/doi.org\/10.1038\/s41597-019-0055-0","journal-title":"Sci Data"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06053-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06053-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06053-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T08:52:36Z","timestamp":1744188756000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06053-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,11]]},"references-count":34,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["6053"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06053-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,11]]},"assertion":[{"value":"6 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}