We propose two semiparametric versions of the debiased Lasso procedure for the model $Y_{i}=X_{i}\beta_{0} g_{0}(Z_{i}) \varepsilon_{i}$, where the parameter vector of interest $\beta_{0}$ is high dimensional but sparse (exactly or approximately) and $g_{0}$ is an unknown nuisance function. Both...