  This paper presents a comparison of classification methods for linguistic
typology for the purpose of expanding an extensive, but sparse language
resource: the World Atlas of Language Structures (WALS) (Dryer and Haspelmath,
2013). We experimented with a variety of regression and nearest-neighbor
methods for use in classification over a set of 325 languages and six syntactic
rules drawn from WALS. To classify each rule, we consider the typological
features of the other five rules; linguistic features extracted from a
word-aligned Bible in each language; and genealogical features (genus and
family) of each language. In general, we find that propagating the majority
label among all languages of the same genus achieves the best accuracy in label
pre- diction. Following this, a logistic regression model that combines
typological and linguistic features offers the next best performance.
Interestingly, this model actually outperforms the majority labels among all
languages of the same family.
