  Working with cognate data involves handling synonyms, that is, multiple words
that describe the same concept in a language. In the early days of language
phylogenetics it was recommended to select one synonym only. However, as we
show here, binary character matrices, which are used as input for computational
methods, do allow for representing the entire dataset including all synonyms.
Here we address the question how one can and if one should include all synonyms
or whether it is preferable to select synonyms a priori. To this end, we
perform maximum likelihood tree inferences with the widely used RAxML-NG tool
and show that it yields plausible trees when all synonyms are used as input.
Furthermore, we show that a priori synonym selection can yield topologically
substantially different trees and we therefore advise against doing so. To
represent cognate data including all synonyms, we introduce two types of
character matrices beyond the standard binary ones: probabilistic binary and
probabilistic multi-valued character matrices. We further show that it is
dataset-dependent for which character matrix type the inferred RAxML-NG tree is
topologically closest to the gold standard. We also make available a Python
interface for generating all of the above character matrix types for cognate
data provided in CLDF format.
