{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T12:32:04Z","timestamp":1779193924051,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,6,17]],"date-time":"2017-06-17T00:00:00Z","timestamp":1497657600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National High Technology Research and Development Program of China","award":["2015AA015405"],"award-info":[{"award-number":["2015AA015405"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61572151"],"award-info":[{"award-number":["61572151"]}],"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":["61602131"],"award-info":[{"award-number":["61602131"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Drug-Named Entity Recognition (DNER) for biomedical literature is a fundamental facilitator of Information Extraction. For this reason, the DDIExtraction2011 (DDI2011) and DDIExtraction2013 (DDI2013) challenge introduced one task aiming at recognition of drug names. State-of-the-art DNER approaches heavily rely on hand-engineered features and domain-specific knowledge which are difficult to collect and define. Therefore, we offer an automatic exploring words and characters level features approach: a recurrent neural network using bidirectional long short-term memory (LSTM) with Conditional Random Fields decoding (LSTM-CRF). Two kinds of word representations are used in this work: word embedding, which is trained from a large amount of text, and character-based representation, which can capture orthographic feature of words. Experimental results on the DDI2011 and DDI2013 dataset show the effect of the proposed LSTM-CRF method. Our method outperforms the best system in the DDI2013 challenge.<\/jats:p>","DOI":"10.3390\/e19060283","type":"journal-article","created":{"date-parts":[[2017,6,19]],"date-time":"2017-06-19T10:29:26Z","timestamp":1497868166000},"page":"283","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":85,"title":["LSTM-CRF for Drug-Named Entity Recognition"],"prefix":"10.3390","volume":"19","author":[{"given":"Donghuo","family":"Zeng","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9081-1410","authenticated-orcid":false,"given":"Chengjie","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingquan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1016\/j.jbi.2011.04.005","article-title":"Using a shallow linguistic kernel for drug-drug interaction extraction","volume":"44","year":"2011","journal-title":"J. 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