  Automatic phylogenetic inference plays an increasingly important role in
computational historical linguistics. Most pertinent work is currently based on
expert cognate judgments. This limits the scope of this approach to a small
number of well-studied language families. We used machine learning techniques
to compile data suitable for phylogenetic inference from the ASJP database, a
collection of almost 7,000 phonetically transcribed word lists over 40
concepts, covering two third of the extant world-wide linguistic diversity.
First, we estimated Pointwise Mutual Information scores between sound classes
using weighted sequence alignment and general-purpose optimization. From this
we computed a dissimilarity matrix over all ASJP word lists. This matrix is
suitable for distance-based phylogenetic inference. Second, we applied cognate
clustering to the ASJP data, using supervised training of an SVM classifier on
expert cognacy judgments. Third, we defined two types of binary characters,
based on automatically inferred cognate classes and on sound-class occurrences.
Several tests are reported demonstrating the suitability of these characters
for character-based phylogenetic inference.
