The　purpose　of　this　paper　is　two-fold.　First,　for　the　estimation　or　inference　about　the　parameters　of　interest　in　semiparametric　models,　the　commonly　used　plug-in　estimation　for　infinite-dimensional　nuisance　parameter　creates　non-negligible　bias,　and　the　least　favorable　curve　or　under-smoothing　is　popularly　employed　for　bias　reduction　in　the　literature.　To　avoid　such　strong　structure　assumptions　on　the　models　and　inconvenience　of　estimation　implementation,　for　the　diverging　number　of　parameters　in　a　varying　coefficient　partially　linear　model,　we　adopt　a　bias-corrected　empirical　likelihood　(BCEL)　in　this　paper.　This　method　results　in　the　distribution　of　the　empirical　likelihood　ratio　to　be　asymptotically　tractable.　It　can　then　be　directly　applied　to　construct　confidence　region　for　the　parameters　of　interest.　Second,　different　from　all　existing　methods　that　impose　strong　conditions　to　ensure　consistency　of　estimation　when　diverging　the　number　of　the　parameters　goes　to　infinity　as　the　sample　size　goes　to　infinity,　we　provide　techniques　to　show　that,　other　than　the　usual　regularity　conditions,　the　consistency　holds　under　moment　conditions　alone　on　the　covariates　and　error　with　a　diverging　rate　being　even　faster　than　those　in　the　literature.　A　simulation　study　is　carried　out　to　assess　the　performance　of　the　proposed　method　and　to　compare　it　with　the　profile　least　squares　method.　A　real　dataset　is　analyzed　for　illustration.　(C)　2011　Elsevier　Inc.　All　rights　reserved.