One of the oldest problems in linguistics is reconstructing the words that appeared in the protolanguages from which modern languages evolved. Identifying the forms of these ancient languages makes it possible to evaluate proposals about the nature of language change and to draw inferences about human history. Protolanguages are typically reconstructed using a painstaking manual process known as the comparative method. We present a family of probabilistic models of sound change as well as algorithms for performing inference in these models. The resulting system automatically and accurately reconstructs protolanguages from modern languages. We apply this system to 637 Austronesian languages, providing an accurate, large-scale automatic reconstruction of a set of protolanguages. Over 85% of the system's reconstructions are within one character of the manual reconstruction provided by a linguist specializing in Austronesian languages. Being able to automatically reconstruct large numbers of languages provides a useful way to quantitatively explore hypotheses about the factors determining which sounds in a language are likely to change over time. We demonstrate this by showing that the reconstructed Austronesian protolanguages provide compelling support for a hypothesis about the relationship between the function of a sound and its probability of changing that was first proposed in 1955. ancestral | computational | diachronic R econstruction of the protolanguages from which modern languages are descended is a difficult problem, occupying historical linguists since the late 18th century. To solve this problem linguists have developed a labor-intensive manual procedure called the comparative method (1), drawing on information about the sounds and words that appear in many modern languages to hypothesize protolanguage reconstructions even when no written records are available, opening one of the few possible windows to prehistoric societies (2, 3). Reconstructions can help in understanding many aspects of our past, such as the technological level (2), migration patterns (4), and scripts (2, 5) of early societies. Comparing reconstructions across many languages can help reveal the nature of language change itself, identifying which aspects of language are most likely to change over time, a long-standing question in historical linguistics (6, 7). In many cases, direct evidence of the form of protolanguages is not available. Fortunately, owing to the world's considerable linguistic diversity, it is still possible to propose reconstructions by leveraging a large collection of extant languages descended from a single protolanguage. Words that appear in these modern languages can be organized into cognate sets that contain words suspected to have a shared ancestral form (Table 1 ). The key observation that makes reconstruction from these data possible is that languages seem to undergo a relatively limited set of regular sound changes, each applied to the entire vocabulary of a language at specific stages of its history (1). Still, several factors make reconstruction a hard problem. For example, sound changes are often context sensitive, and many are string insertions and deletions. In this paper, we present an automated system capable of large-scale reconstruction of protolanguages directly from words that appear in modern languages. This system is based on a probabilistic model of sound change at the level of phonemes, building on work on the reconstruction of ancestral sequences and alignment in computational biology (8-12). Several groups have recently explored how methods from computational biology can be applied to problems in historical linguistics, but such work has focused on identifying the relationships between languages (as might be expressed in a phylogeny) rather than reconstructing the languages themselves (13-18). Much of this type of work has been based on binary cognate or structural matrices (19, 20) , which discard all information about the form that words take, simply indicating whether they are cognate. Such models did not have the goal of reconstructing protolanguages and consequently use a representation that lacks the resolution required to infer ancestral phonetic sequences. Using phonological representations allows us to perform reconstruction and does not require us to assume that cognate sets have been fully resolved as a preprocessing step. Representing the words at each point in a phylogeny and having a model of how they change give a way of comparing different hypothesized cognate sets and hence inferring cognate sets automatically. The focus on problems other than reconstruction in previous computational approaches has meant that almost all existing protolanguage reconstructions have been done manually. However, to obtain more accurate reconstructions for older languages, large numbers of modern languages need to be analyzed. The Proto-Austronesian language, for instance, has over 1,200 descendant languages (21). All of these languages could potentially increase the quality of the reconstructions, but the number of possibilities increases considerably with each language, making it difficult to analyze a large number of languages simultaneously. The few previous systems for automated reconstruction of protolanguages or cognate inference (22-24) were unable to handle this increase in computational complexity, as they relied on deterministic models of sound change and exact but intractable algorithms for reconstruction. Being able to reconstruct large numbers of languages also makes it possible to provide quantitative answers to questions about the factors that are involved in language change. We demonstrate the potential for automated reconstruction to lead to novel results in historical linguistics by investigating a specific hypothesized regularity in sound changes called functional load. The functional load hypothesis, introduced in 1955, asserts that sounds that play a more important role in distinguishing words are less likely to change over time (6). Our probabilistic reconstruction of hundreds of protolanguages in the Austronesian phylogeny provides a way to explore this question quantitatively, producing compelling evidence in favor of the functional load hypothesis.
REF used probabilistic models to trace language change in the Austronesian languages, based on a given phylogenetic tree.
Automated reconstruction of ancient languages using probabilistic models of sound change
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America
Computer Science Medicine