Biomedical　named　entity　recognition　(BNER)　is　a　critical　task　for　biomedical　information　extraction.　Most　popular　BNER　approaches　based　on　deep　learning　utilize　words　and　characters　as　features　to　represent　medical　texts.　However,　many　medical　terminologies　are　composed　of　multiple　words　and　characters,　and　splitting　medical　terminology　into　multiple　words　(or　characters)　and　assigning　weight　values　for　each　word　(or　character)　by　a　standard　attention　mechanism　may　disperse　the　attention　score　and　result　in　a　lower　weight　value　for　the　medical　terminology.　This　paper　proposes　a　Dictionary-guided　Attention　Network　(DGAN)　for　BNER　in　Chinese　electronic　medical　records　(EMRs).　First,　the　medical　concepts　are　extracted　as　large-size　words　to　supplement　the　comprehensive　semantic　information　of　the　medical　terminology　by　matching　the　EMR　text　to　the　biomedical　dictionary.　Then,　based　on　the　matched　dictionary　results,　an　optimized　attention　strategy　is　proposed　to　focus　on　the　medical　concept　and　adaptively　assign　higher　weights　to　the　characters　contained　in　a　concept.　Furthermore,　semisupervised　learning　is　introduced　to　reduce　the　manual　labeling　of　data　and　to　handle　the　entities　not　defined　in　the　medical　dictionary.　To　validate　our　new　model　in　recognizing　biomedical　named　entities,　we　conduct　comprehensive　experiments　on　a　real-world　Chinese　EMR　dataset　and　the　CCKS2017　dataset.　Our　promising　results　illustrate　that　our　method　not　only　achieves　a　state-of-the-art　performance　in　BNER　but　also　reduces　manual　data　annotation.　©　2023　Elsevier　Ltd