{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:55:07Z","timestamp":1783184107572,"version":"3.54.6"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"16","license":[{"start":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T00:00:00Z","timestamp":1656288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Ministry of Science and Technology of China","award":["2021ZD0113602"],"award-info":[{"award-number":["2021ZD0113602"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176014"],"award-info":[{"award-number":["62176014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1836206"],"award-info":[{"award-number":["U1836206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,8,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Biomedical Named Entity Recognition (BioNER) aims to identify biomedical domain-specific entities (e.g. gene, chemical and disease) from unstructured texts. Despite deep learning-based methods for BioNER achieving satisfactory results, there is still much room for improvement. Firstly, most existing methods use independent sentences as training units and ignore inter-sentence context, which usually leads to the labeling inconsistency problem. Secondly, previous document-level BioNER works have approved that the inter-sentence information is essential, but what information should be regarded as context remains ambiguous. Moreover, there are still few pre-training-based BioNER models that have introduced inter-sentence information. Hence, we propose a cache-based inter-sentence model called BioNER-Cache to alleviate the aforementioned problems.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We propose a simple but effective dynamic caching module to capture inter-sentence information for BioNER. Specifically, the cache stores recent hidden representations constrained by predefined caching rules. And the model uses a query-and-read mechanism to retrieve similar historical records from the cache as the local context. Then, an attention-based gated network is adopted to generate context-related features with BioBERT. To dynamically update the cache, we design a scoring function and implement a multi-task approach to jointly train our model. We build a comprehensive benchmark on four biomedical datasets to evaluate the model performance fairly. Finally, extensive experiments clearly validate the superiority of our proposed BioNER-Cache compared with various state-of-the-art intra-sentence and inter-sentence baselines.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availabilityand implementation<\/jats:title><jats:p>Code will be available at https:\/\/github.com\/zgzjdx\/BioNER-Cache.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac422","type":"journal-article","created":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T12:32:50Z","timestamp":1656333170000},"page":"3976-3983","source":"Crossref","is-referenced-by-count":9,"title":["Improving biomedical named entity recognition by dynamic caching inter-sentence information"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6671-9208","authenticated-orcid":false,"given":"Yiqi","family":"Tong","sequence":"first","affiliation":[{"name":"Institute of Artificial Intelligence, Beihang University , Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuzhen","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence, Beihang University , Beijing 100191, China"},{"name":"SKLSDE, School of Computer Science, Beihang University , Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huajie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence, Beihang University , Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuyu","family":"Fang","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence, Beihang University , Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics , Chengdu 611130, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deqing","family":"Wang","sequence":"additional","affiliation":[{"name":"SKLSDE, School of Computer Science, Beihang University , Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hengshu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Baidu Inc , Beijing 100085, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Ni","sequence":"additional","affiliation":[{"name":"Xiamen Data Intelligence Academy of ICT, CAS , Xiamen 361021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"key":"2023041408491720200_","first-page":"3615","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"Beltagy","year":"2019"},{"key":"2023041408491720200_","first-page":"4171","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)","author":"Devlin","year":"2019"},{"key":"2023041408491720200_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jbi.2013.12.006","article-title":"NCBI disease corpus: a resource for disease name recognition and concept normalization","volume":"47","author":"Do\u011fan","year":"2014","journal-title":"J. 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