  This paper describes the NEMO submission to SIGTYP 2020 shared task which
deals with prediction of linguistic typological features for multiple languages
using the data derived from World Atlas of Language Structures (WALS). We
employ frequentist inference to represent correlations between typological
features and use this representation to train simple multi-class estimators
that predict individual features. We describe two submitted ridge
regression-based configurations which ranked second and third overall in the
constrained task. Our best configuration achieved the micro-averaged accuracy
score of 0.66 on 149 test languages.
