{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:20:38Z","timestamp":1778347238000,"version":"3.51.4"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"content-version":"vor","delay-in-days":50,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Europe research and innovation programme","award":["101100615"],"award-info":[{"award-number":["101100615"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study evaluates the usefulness of explicit syntactic knowledge, integrated via a neural mechanism, in improving the accuracy of named entity recognition in the domain of biomedical text processing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>Syntactic structure of a text can be helpful to determine whether a certain part of the text is an entity or not. Parsing is an essential technique in natural language processing (NLP) that can be utilized to determine the syntactic structure of sentences in human languages. We propose to infuse syntactic knowledge through the attention mechanism using dependency parsing and sequence labelling parsing, as well as the multi-task learning paradigm. Experiments were conducted on five datasets: MTSamples, VAERS, NCBI-disease, BC2GM, and JNLPBA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We demonstrate improvements in the F1 score over the current state of the art on 3 out of 5 datasets (MTSamples, VAERS, and NCBI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>We reduce the number of mismatches with gold labels in particular in the n-dash and parentheses tokens and in compound and adjective modifier dependencies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Syntactic features improve NER accuracy in attention-based neural systems, and parsing as sequence labelling brings additional benefits.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamiaopen\/ooaf149","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T11:26:50Z","timestamp":1761046010000},"source":"Crossref","is-referenced-by-count":1,"title":["SynNER: syntax-infused named entity recognition in the biomedical domain"],"prefix":"10.1093","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4124-7929","authenticated-orcid":false,"given":"Muhammad","family":"Imran","sequence":"first","affiliation":[{"name":"Universidade da Coru\u00f1a, CITIC, Departamento de Ciencias de la Computaci\u00f3n y Tecnolog\u00edas de la Informaci\u00f3n , Campus de Elvi\u00f1a s\/n , A Coru\u00f1a 15071,","place":["Spain"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9969-058X","authenticated-orcid":false,"given":"Olga","family":"Zamaraeva","sequence":"additional","affiliation":[{"name":"Universidade da Coru\u00f1a, CITIC, Departamento de Ciencias de la Computaci\u00f3n y Tecnolog\u00edas de la Informaci\u00f3n , Campus de Elvi\u00f1a s\/n , A Coru\u00f1a 15071,","place":["Spain"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0752-8812","authenticated-orcid":false,"given":"Carlos","family":"G\u00f3mez-Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Universidade da Coru\u00f1a, CITIC, Departamento de Ciencias de la Computaci\u00f3n y Tecnolog\u00edas de la Informaci\u00f3n , Campus de Elvi\u00f1a s\/n , A Coru\u00f1a 15071,","place":["Spain"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2026,2,21]]},"reference":[{"key":"2026022502161297300_ooaf149-B1","author":"Grishman","year":"1996"},{"key":"2026022502161297300_ooaf149-B2","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/TKDE.2020.2981314","article-title":"A survey on deep learning for named entity recognition","volume":"34","author":"Li","year":"2022","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2026022502161297300_ooaf149-B3","first-page":"1314","author":"G\u00f3mez-Rodr\u00edguez","year":"2018"},{"key":"2026022502161297300_ooaf149-B4","first-page":"717","author":"Strzyz","year":"2019"},{"key":"2026022502161297300_ooaf149-B5","doi-asserted-by":"crossref","first-page":"btae163","DOI":"10.1093\/bioinformatics\/btae163","article-title":"Advancing entity recognition in biomedicine via instruction tuning of large language models","volume":"40","author":"Keloth","year":"2024","journal-title":"Bioinformatics"},{"key":"2026022502161297300_ooaf149-B6","doi-asserted-by":"crossref","first-page":"1812","DOI":"10.1093\/jamia\/ocad259","article-title":"Improving large language models for clinical named entity recognition via prompt engineering","volume":"31","author":"Hu","year":"2024","journal-title":"J Am Med Inform Assoc"},{"key":"2026022502161297300_ooaf149-B7","doi-asserted-by":"crossref","first-page":"7697","DOI":"10.1038\/s41598-024-58334-x","article-title":"BioBBC: a multi-feature model that enhances the detection of biomedical entities","volume":"14","author":"Alamro","year":"2024","journal-title":"Sci Rep"},{"key":"2026022502161297300_ooaf149-B8","first-page":"1730","author":"Dai","year":"2024"},{"key":"2026022502161297300_ooaf149-B9","doi-asserted-by":"crossref","first-page":"104789","DOI":"10.1016\/j.jbi.2025.104789","article-title":"Improving entity recognition using ensembles of deep learning and fine-tuned large language models: a case study on adverse event extraction from VAERS and social media","volume":"163","author":"Li","year":"2025","journal-title":"J Biomed Inform"},{"key":"2026022502161297300_ooaf149-B10","doi-asserted-by":"crossref","first-page":"btad103","DOI":"10.1093\/bioinformatics\/btad103","article-title":"On the effectiveness of compact biomedical transformers","volume":"39","author":"Rohanian","year":"2023","journal-title":"Bioinformatics"},{"key":"2026022502161297300_ooaf149-B11","first-page":"1098","author":"Mu","year":"2024"},{"key":"2026022502161297300_ooaf149-B12","doi-asserted-by":"crossref","first-page":"100426","DOI":"10.1016\/j.dajour.2024.100426","article-title":"A clinical named entity recognition model using pretrained word embedding and deep neural networks","volume":"10","author":"Dash","year":"2024","journal-title":"Decision Analytics Journal"},{"key":"2026022502161297300_ooaf149-B13","doi-asserted-by":"crossref","first-page":"104731","DOI":"10.1016\/j.jbi.2024.104731","article-title":"Augmenting biomedical named entity recognition with general-domain resources","volume":"159","author":"Yin","year":"2024","journal-title":"J Biomed Inform"},{"key":"2026022502161297300_ooaf149-B14","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1093\/jamia\/ocae095","article-title":"RT: a retrieving and chain-of-thought framework for few-shot medical named entity recognition","volume":"31","author":"Li","year":"2024","journal-title":"J Am Med Informat Associat"},{"key":"2026022502161297300_ooaf149-B15","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1186\/s12859-024-05710-z","article-title":"GPDminer: a tool for extracting named entities and analyzing relations in biological literature","volume":"25","author":"Park","year":"2024","journal-title":"BMC Bioinformatics"},{"key":"2026022502161297300_ooaf149-B16","author":"Sasano","year":"2008"},{"key":"2026022502161297300_ooaf149-B17","first-page":"94","author":"Ling","year":"2012"},{"key":"2026022502161297300_ooaf149-B18","first-page":"4231","author":"Nie"},{"key":"2026022502161297300_ooaf149-B19","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1093\/bioinformatics\/btx761","article-title":"An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition","volume":"34","author":"Luo","year":"2018","journal-title":"Bioinformatics"},{"key":"2026022502161297300_ooaf149-B20","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1186\/s12859-022-05051-9","article-title":"BioByGANS: biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework","volume":"23","author":"Zheng","year":"2022","journal-title":"BMC Bioinformatics"},{"key":"2026022502161297300_ooaf149-B21","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1186\/s12859-020-03834-6","article-title":"Improving biomedical named entity recognition with syntactic information","volume":"21","author":"Tian","year":"2020","journal-title":"BMC Bioinformatics"},{"key":"2026022502161297300_ooaf149-B22","doi-asserted-by":"crossref","first-page":"3069","DOI":"10.1038\/s41598-025-86718-0","article-title":"Attention-based interactive multi-level feature fusion for named entity recognition","volume":"15","author":"Xu","year":"2025","journal-title":"Sci Rep"},{"key":"2026022502161297300_ooaf149-B23","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.3390\/app11031090","article-title":"On the use of parsing for named entity recognition","volume":"11","author":"Alonso","year":"2021","journal-title":"Applied Sciences"},{"key":"2026022502161297300_ooaf149-B24","doi-asserted-by":"crossref","first-page":"3280","DOI":"10.1038\/s41467-025-56989-2","article-title":"Benchmarking large language models for biomedical natural language processing applications and recommendations","volume":"16","author":"Chen","year":"2025","journal-title":"Nat Commun"},{"key":"2026022502161297300_ooaf149-B25","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1038\/s41746-024-01377-1","article-title":"Clinical entity augmented retrieval for clinical information extraction","volume":"8","author":"Lopez","year":"2025","journal-title":"NPJ Digit Med"},{"key":"2026022502161297300_ooaf149-B26","first-page":"4034","volume-title":"Proceedings of the Twelfth Language Resources and Evaluation Conference","author":"Nivre","year":"2020"},{"key":"2026022502161297300_ooaf149-B27","doi-asserted-by":"crossref","first-page":"3229","DOI":"10.18653\/v1\/2020.acl-main.295","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"Wang","year":"2020"},{"key":"2026022502161297300_ooaf149-B28","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.3519","volume-title":"PeerJ Comput Sci","author":"Imran","year":"2026"},{"key":"2026022502161297300_ooaf149-B29","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1017\/S1351324900002370","article-title":"On building a more efficient grammar by exploiting types","volume":"6","author":"Flickinger","year":"2000","journal-title":"Nat Lang Eng"},{"key":"2026022502161297300_ooaf149-B30","first-page":"31","volume-title":"Language from a Cognitive Perspective: Grammar, Usage and Processing","author":"Flickinger","year":"2011"},{"key":"2026022502161297300_ooaf149-B31","first-page":"12","author":"G\u00f3mez-Rodr\u00edguez"},{"key":"2026022502161297300_ooaf149-B32","first-page":"83","volume-title":"Proceedings of V XoveTIC Conference. XoveTIC 2022","author":"Roca","year":"2023"},{"key":"2026022502161297300_ooaf149-B33","first-page":"176","author":"Van Der Goot"}],"container-title":["JAMIA Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jamiaopen\/article-pdf\/9\/1\/ooaf149\/67102987\/ooaf149.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jamiaopen\/article-pdf\/9\/1\/ooaf149\/67102987\/ooaf149.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T07:16:23Z","timestamp":1772003783000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamiaopen\/article\/doi\/10.1093\/jamiaopen\/ooaf149\/8496818"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,6]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1,6]]}},"URL":"https:\/\/doi.org\/10.1093\/jamiaopen\/ooaf149","relation":{},"ISSN":["2574-2531"],"issn-type":[{"value":"2574-2531","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,2]]},"published":{"date-parts":[[2026,1,6]]},"article-number":"ooaf149"}}