We claim that making sense of the typological diversity of languages demands a historical/evolutionary approach. We are pleased that the target paper (Dunn et al. 2011a) has served to bring discussion of this claim into prominence, and are grateful that leading typologists have taken the time to respond (commentaries denoted by boldface). It is unfortunate though that a number of the commentaries in this special issue show significant misunderstandings of our paper.
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In the following section we try to explain the basic underlying reasoning, turning in the remaining sections to some of these recurrent points of contention. In the final section, we collect some responses to points made in individual commentaries that did not fit neatly into the body of our response.

The Polynesian lexicon project, POLLEX, was initiated in 1965 by Bruce Biggs in order to provide a large-scale comparative dictionary of Polynesian languages. Since then, POLLEX has grown to include over 55,000 reflexes of more than 4,700 reconstructed forms in 68 languages. These data have enabled many fundamental advances in Polynesian linguistics and prehistory. At almost half a century old, POLLEX is one of the longest-standing databases of linguistic information, and has moved through various incarnations, from type- writer and edge-punched cards, through microfiche to mainframe computer. In the last few years, online databases of linguistic information have become increasingly more prevalent, representing a major shift in the way linguistics is conducted. Online databases provide many advantages over the older forms of data distribution, including high availability, more robust data storage, and easy data manipulation and searching, and they also facilitate the replication of previous studies. This paper announces the latest reincarnation of the POLLEX database as an online resource, POLLEX-Online (http://pollex.org.nz), and describes the technical implementation details.

The Levenshtein distance is a simple distance metric derived from the number of edit operations needed to transform one string into another. This metric has received recent attention as a means of automatically classifying languages into genealogical subgroups. In this paper I test the performance of the Levenshtein distance for classifying languages by subsampling three language subsets from a large database of Austronesian languages. Comparing the classification proposed by the Levenshtein distance to that of the comparative method shows that the Levenshtein classification is correct only 40% of time. Standardising the orthography increases the performance, but only to a maximum of 65% accuracy within language subgroups. The accuracy of the Levenshtein classification decreases rapidly with phylogenetic distance, failing to discriminate homology and chance similarity across distantly related languages. This poor performance suggests the need for more linguistically nuanced methods for automated language classification tasks.

The Abstract:

Languages vary widely but not without limit. The central goal of linguistics is to describe the diversity of human languages and explain the constraints on that diversity. Generative linguists following Chomsky have claimed that linguistic diversity must be constrained by innate parameters that are set as a child learns a language. In contrast, other linguists following Greenberg have claimed that there are statistical tendencies for co-occurrence of traits reflecting universal systems biases, rather than absolute constraints or parametric variation. Here we use computational phylogenetic methods to address the nature of constraints on linguistic diversity in an evolutionary framework. First, contrary to the generative account of parameter setting, we show that the evolution of only a few word-order features of languages are strongly correlated. Second, contrary to the Greenbergian generalizations, we show that most observed functional dependencies between traits are lineage-specific rather than universal tendencies. These findings support the view that—at least with respect to word order—cultural evolution is the primary factor that determines linguistic structure, with the current state of a linguistic system shaping and constraining future states.

In this paper we outline two debates about the nature of human cultural history. The first focuses on the extent to which human history is treelike (its shape), and the second on the unity of that history (its fabric).

Proponents of cultural phylogenetics are often accused of assuming that human history has been both highly tree-like and consists of tightly linked lineages. Critics have pointed out obvious exceptions to these assumptions.

Instead of a priori dichotomous disputes about the validity of cultural phylogenetics phylogenies, we suggest that the debate is better conceptualized as involving positions along continuous dimensions. The challenge for empirical research is therefore to determine where particular aspects of culture lie on these dimensions.

We discuss the ability of current computational methods derived from evolutionary biology to address these questions. These methods are then used to compare the extent to which lexical evolution is treelike in different parts of the world and to evaluate the coherence of cultural and linguistic lineages.

There are approximately 7000 languages spoken in the world today. This diversity reflects the legacy of thousands of years of cultural evolution. How far back we can trace this history depends largely on the rate at which the different components of language evolve. Rates of lexical evolution are widely thought to impose an upper limit of 6-10 thousand years on reliably identifying language relationships. In contrast, it has been argued that certain structural elements of language are much more stable. Just as biologists use highly conserved genes to uncover the deepest branches in the tree of life, highly stable linguistic features hold the promise of identifying deep relationships between the world’s languages.
Here we present the first global network of languages based on this typological information. We evaluate the relative evolutionary rates of both typological and lexical features in the Austronesian and Indo-European language families. The first indications are that typological features evolve at similar rates to basic vocabulary but their evolution is substantially less treelike. Our results suggest that, whilst rates of vocabulary change are correlated between the two language families, the rates of evolution of typological features and structural sub-types show no consistent relationship across families.

You can download a preprint of this paper here.

A network showing the relationships between 99 languages across the world. This network is built from structural information about language properties (phonology, morphology, grammar and lexicon). The length of the branches is proportional to the amount of divergence between the languages, and the box-like structures on the network show conflicting signal due to borrowing between languages and chance similarity.

Many papers about language subgrouping make the argument that if a word form is present in two language subgroups, then it probably reflects their common proto-language. This fairly reasonable assumption has taken center-stage in the reconstruction of Proto-Central-Pacific and Proto-Oceanic, and subsequent inferences about these respective societies. In this talk I will introduce a method that calculates the actual probability that a given form reflects the proto-language. This method builds on the linguistic comparative method, and provides a powerful tool for testing and confirming reconstructions in a probabilistic framework.

Historical linguistics has never been particularly intimate with computers. The first wave of computational historical linguistics—lexicostatistics—was developed in the 1950s and quickly applied to language groups around the world from Indo-European to Austronesian. However, critics were quick to point out the problems caused by assuming a single constant rate of lexical replacement and repeatedly noted the erroneous results that this produced. As a consequence of these critiques lexicostatistics has been widely rejected by mainstream historical linguists. The last few years have seen a second wave of computational approaches entering historical linguistics: phylogenetic methods. These techniques, drawn from evolutionary biology, have been used to investigate some provocative and controversial claims about human prehistory. Given the combination of strong claims, new techniques, and the high-profile reporting of results, it is not surprising that these studies are often controversial. Sadly many of these criticisms are mired in misunderstanding.

Computational phylogenetic methods are not just lexicostatistics redux, but a powerful supplement to the comparative method used in historical linguistics. Here we will focus on one of the great battlegrounds between lexicostatistics and the traditional comparative method: the Austronesian language family. First, we will describe how Bayesian phylogenetic methods work, and then give a step-by-step explanation of an analysis of a large lexical dataset for 400 Austronesian languages.

Phylogenetic methods have recently been applied to studies of cultural evolution. However, it has been claimed that the large amount of horizontal transmission that sometimes occurs between cultural groups invalidates the use of these methods. Here, we use a natural model of linguistic evolution to simulate borrowing between languages. The results show that tree topologies constructed with Bayesian phylogenetic methods are robust to realistic levels of borrowing. Inferences about divergence dates are slightly less robust and show a tendency to underestimate dates. Our results demonstrate that realistic levels of reticulation between cultures do not invalidate a phylogenetic approach to cultural and linguistic evolution.

Phylogenetic methods have revolutionised evolutionary biology and have recently been applied to studies of linguistic and cultural evolution. However, the basic comparative data on the languages of the world required for these analyses is often widely dispersed in hard to obtain sources. Here we outline how our Austronesian Basic Vocabulary Database (ABVD) helps remedy this situation by collating wordlists from over 500 languages into one web-accessible database. We describe the technology underlying the ABVD and discuss the benefits that an evolutionary bioinformatic approach can provide. These include facilitating computational comparative linguistic research, answering questions about human prehistory, enabling syntheses with genetic data, and safe-guarding fragile linguistic information.