This paper proposes a constrained empirical likelihood confidence region for a
parameter in the semi-linear errors-in-variables model. The confidence region is constructed by
combining the score function corresponding to the squared orthogonal distance with a constraint on
the parameter, and it overcomes that the solution of limiting mean estimation equations is not
unique. It is shown that the empirical log likelihood ratio at the true parameter converges to the
standard chi-square distribution. Simulations show that the proposed confidence region has
coverage probability which is closer to the nominal level, as well as narrower than those of normal
approximation of generalized least squares estimator in most cases. A real data example is given
