Finding Cognate Groups Using Phylogenies

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1030–1039, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Finding Cognate Groups using Phylogenies David Hall and Dan Klein Computer Science Division University of California, Berkeley {dlwh,klein}@cs.berkeley.edu Abstract A central problem in historical linguistics is the identification of historically related cognate words. We present a generative phylogenetic model for automatically in- ducing cognate group structure from un- aligned word lists. Our model represents the process of transformation and trans- mission from ancestor word to daughter word, as well as the alignment between the words lists of the observed languages. We also present a novel method for sim- plifying complex weighted automata cre- ated during inference to counteract the otherwise exponential growth of message sizes. On the task of identifying cognates in a dataset of Romance words, our model significantly outperforms a baseline ap- proach, increasing accuracy by as much as 80%. Finally, we demonstrate that our au- tomatically induced groups can be used to successfully reconstruct ancestral words. 1 Introduction A crowning achievement of historical linguistics is the comparative method (Ohala, 1993), wherein linguists use word similarity to elucidate the hid- den phonological and morphological processes which govern historical descent. The comparative method requires reasoning about three important hidden variables: the overall phylogenetic guide tree among languages, the evolutionary parame- ters of the ambient changes at each branch, and the cognate group structure that specifies which words share common ancestors. All three of these variables interact and inform each other, and so historical linguists often con- sider them jointly. However, linguists are cur- rently required to make qualitative judgments re- garding the relative likelihood of certain sound changes, cognate groups, and so on. Several re- cent statistical methods have been introduced to provide increased quantitative backing to the com- parative method (Oakes, 2000; Bouchard-Cˆot´e et al., 2007; Bouchard-Cˆot´e et al., 2009); others have modeled the spread of language changes and spe- ciation (Ringe et al., 2002; Daum´e III and Camp- bell, 2007; Daum´e III, 2009; Nerbonne, 2010). These automated methods, while providing ro- bustness and scale in the induction of ancestral word forms and evolutionary parameters, assume that cognate groups are already known. In this work, we address this limitation, presenting a model in which cognate groups can be discovered automatically. Finding cognate groups is not an easy task, because underlying morphological and phonolog- ical changes can obscure relationships between words, especially for distant cognates, where sim- ple string overlap is an inadequate measure of sim- ilarity. Indeed, a standard string similarity met- ric like Levenshtein distance can lead to false positives. Consider the often cited example of Greek /ma:ti/ and Malay /mata/, both meaning “eye” (Bloomfield, 1938). If we were to rely on Levenshtein distance, these words would seem to be a highly attractive match as cognates: they are nearly identical, essentially differing in only a sin- gle character. However, no linguist would posit that these two words are related. To correctly learn that …

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