Empirical　likelihood-based　inference　for　the　nonparametric　components　in　additive　partially　linear　models　is　investigated.　An　empirical　likelihood　approach　to　construct　the　confidence　intervals　of　the　nonparametric　components　is　proposed　when　the　linear　covariate　is　measured　with　and　without　errors.　We　show　that　the　proposed　empirical　log-likelihood　ratio　is　asymptotically　standard　chi-squared　without　requiring　the　undersmoothing　of　the　nonparametric　components.　Then,　it　can　be　directly　used　to　construct　the　confidence　intervals　for　the　nonparametric　functions.　A　simulation　study　indicates　that,　compared　with　a　normal　approximation-based　approach,　the　proposed　method　works　better　in　terms　of　coverage　probabilities　and　widths　of　the　pointwise　confidence　intervals.