• Current efforts in computational historical linguistics are predominantly concerned
    with phylogenetic inference. Methods for ancestral state reconstruction have only
    been applied sporadically. In contrast to phylogenetic algorithms, automatic reconstruction methods presuppose phylogenetic information in order to explain what has
    evolved when and where. Here we report a pilot study exploring how well automatic
    methods for ancestral state reconstruction perform in the task of onomasiological
    reconstruction in multilingual word lists, where algorithms are used to infer how the
    words evolved along a given phylogeny, and reconstruct which cognate classes were
    used to express a given meaning in the ancestral languages. Comparing three different methods, Maximum Parsimony, Minimal Lateral Networks, and Maximum Likeli-
    hood on three different test sets (Indo-European, Austronesian, Chinese) using binary
    and multi-state coding of the data as well as single and sampled phylogenies, we find
    that Maximum Likelihood largely outperforms the other methods. At the same time,
    however, the general performance was disappointingly low, ranging between 0.66 (Chinese) and 0.79 (Austronesian) for the F-Scores. A closer linguistic evaluation of the
    reconstructions proposed by the best method and the reconstructions given in the gold
    standards revealed that the majority of the cases where the algorithms failed can be
    attributed to problems of independent semantic shift (homoplasy), to morphological
    processes in lexical change, and to wrong reconstructions in the independently created
    test sets that we employed.