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Automatic phylogenetic inference plays an increasingly important role incomputational historical linguistics. Most pertinent work is currently based onexpert cognate judgments. This limits the scope of this approach to a smallnumber of well-studied language families. We used machine learning techniquesto compile data suitable for phylogenetic inference from the ASJP database, acollection of almost 7,000 phonetically transcribed word lists over 40concepts, covering two third of the extant world-wide linguistic diversity.First, we estimated Pointwise Mutual Information scores between sound classesusing weighted sequence alignment and general-purpose optimization. From thiswe computed a dissimilarity matrix over all ASJP word lists. This matrix issuitable for distance-based phylogenetic inference. Second, we applied cognateclustering to the ASJP data, using supervised training of an SVM classifier onexpert 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 charactersfor character-based phylogenetic inference.

Global-scale phylogenetic linguistic inference from lexical resources