Open and semi-open response formats are part of almost all surveys in the social, behavioral, educational and economic sciences. Generally, they serve the empirical operationalization of theoretical constructs for which it is not possible to adequately represent all relevant response options in the instrument. The (post-)usability of such data for quantitative analyses largely depends on the - typically retrospective - coding of the textual information and the subsequent derivation of standard variables. Manual coding in this context is a time-consuming, error-prone, and costly task in the face of hundreds or even thousands of categories. An example is occupational information (e.g., job and occupation titles), the provision of which in the form of relevant classifications (e.g., ISCO, KldB) and derived status, class, or prestige indicators (e.g., ISEI, SIOPS, EGP class scheme, CAMSIS) significantly increases the analytical potential of research data. In particular, panel studies with extensive educational and employment biographies face the challenge of having to code large amounts of textual entries in a high-quality and consistent manner within a short period of time.