Recently,　lexicon-enhanced　methods　for　Chinese　Named　Entity　Recognition　(NER)　have　achieved　great　success　which　requires　a　high-quality　lexicon.　However,　for　the　domain-specific　Chinese　NER,　it　is　challenging　to　obtain　such　a　high-quality　lexicon　due　to　the　different　distribution　between　the　general　lexicon　and　domain-specific　data,　and　the　high　construction　cost　of　the　domain　lexicon.　To　address　these　challenges,　we　introduce　dual-source　lexicons　(i.e.,　a　general　lexicon　and　a　domain　lexicon)　to　acquire　enriched　lexical　knowledge.　Considering　that　the　general　lexicon　often　contains　more　noise　compared　to　its　domain　counterparts,　we　further　propose　a　dual-stream　model,　Dual　Flat-LAttice　Transformer　(DualFLAT),　designed　to　mitigate　the　impact　of　noise　originating　from　the　general　lexicon　while　comprehensively　harnessing　the　knowledge　contained　within　the　dual-source　lexicons.　Experimental　results　on　three　public　domain-specific　Chinese　NER　datasets　(i.e.,　News,　Novel　and　E-commerce)　demonstrate　that　our　method　consistently　outperforms　the　single-source　lexicon-enhanced　approaches,　achieving　state-of-the-art　results.　Specifically,　our　proposed　DualFLAT　model　consistently　outperforms　the　baseline　FLAT,　with　an　increase　of　up　to　1.52%,　4.84%　and　1.34%　in　F1　score　for　the　News,　Novel　and　E-commerce　datasets,　respectively.　©　2024