This article presents a new approach for estimating the policy positions of political actors in the German multi-party policy space. The approach consists of two steps, 'smart tagging' in the data generation process and Bayesian factor analysis in the estimation process. 'Smart tagging' relates the statements of political parties and governments to the keywords of German federal legislation, which we use to estimate the policy positions in portfolio-specific n-dimensional policy spaces. Our G-LIS approach (German 'LexIconSpace') provides several advantages for scholars evaluating policy-seeking theories, in particular by providing context-related variation of policy positions across portfolios and over time. Our findings for the portfolio of 'labour and social policy' reveal a two-factor solution which unfolds a latent 'resource' and 'value' dimension in Germany during the period from 1961 to 2009. We find changes in the policy positions of German political parties and governments, which existing approaches can hardly identify in n-dimensional spaces under the specification of the error term for each dimension and actor.