Quantitative analyses of typological data

2010

In the thesis it is discussed in what ways concepts and methodology developed in evolutionary biology can be applied to the explanation and research of language change. The parallel nature of the mechanisms of biological evolution and language change is explored along with the history of the exchange of ideas between these two disciplines. Against this background computational methods developed in evolutionary biology are taken into consideration in terms of their applicability to the study of historical relationships between languages. Different phylogenetic methods are explained in common terminology, avoiding the technical language of statistics. The thesis is on one hand a synthesis of earlier scientific discussion, and on the other an attempt to map out the problems of earlier approaches in addition to finding new guidelines in the study of language change on their basis. Primarily literature about the connections between evolutionary biology and language change, along with research articles describing applications of phylogenetic methods into language change have been used as source material. The thesis starts out by describing the initial development of the disciplines of evolutionary biology and historical linguistics, a process which right from the beginning can be seen to have involved an exchange of ideas concerning the mechanisms of language change and biological evolution. The historical discussion lays the foundation for the handling of the generalised account of selection developed during the recent few decades. This account is aimed for creating a theoretical framework capable of explaining both biological evolution and cultural change as selection processes acting on self-replicating entities. This thesis focusses on the capacity of the generalised account of selection to describe language change as a process of this kind. In biology, the mechanisms of evolution are seen to form populations of genetically related organisms through time. One of the central questions explored in this thesis is whether selection theory makes it possible to picture languages are forming populations of a similar kind, and what a perspective like this can offer to the understanding of language in general. In historical linguistics, the comparative method and other, complementing methods have been traditionally used to study the development of languages from a common ancestral language. Computational, quantitative methods have not become widely used as part of the central methodology of historical linguistics. After the fading of a limited popularity enjoyed by the lexicostatistical method since the 1950s, only in the recent years have also the computational methods of phylogenetic inference used in evolutionary biology been applied to the study of early language history. In this thesis the possibilities offered by the traditional methodology of historical linguistics and the new phylogenetic methods are compared. The methods are approached through the ways in which they have been applied to the Indo-European languages, which is the most thoroughly investigated language family using both the traditional and the phylogenetic methods. The problems of these applications along with the optimal form of the linguistic data used in these methods are explored in the thesis. The mechanisms of biological evolution are seen in the thesis as parallel in a limited sense to the mechanisms of language change, however sufficiently so that the development of a generalised account of selection is deemed as possibly fruiful for understanding language change. These similarities are also seen to support the validity of using phylogenetic methods in the study of language history, although the use of linguistic data and the models of language change employed by these models are seen to await further development.

Dunn and colleagues (2008) describe and exemplify the use of sophisticated analyses of abstract structural features to reconstruct language histories. The techniques that they use do show some clustering in the groups of languages that they examine; Dunn et al. state that they 'tend to favor a phylogenetic origin for the signal of relatedness' (p. 748), and that the results of their test case 'show a close degree of correspondence to the existing linguistic classification based on sound-meaning correspondences' (p. 747). We argue that a more parsimonious explanation for the results obtained by Dunn et al.'s methodology is that it accurately maps linguistic geography, the network of contact and diffusion that postdates a proto-language, in most cases corresponding to geographic distance.*

BMC Evolutionary Biology, 2016

Background: Curious parallels between the processes of species and language evolution have been observed by many researchers. Retracing the evolution of Indo-European (IE) languages remains one of the most intriguing intellectual challenges in historical linguistics. Most of the IE language studies use the traditional phylogenetic tree model to represent the evolution of natural languages, thus not taking into account reticulate evolutionary events, such as language hybridization and word borrowing which can be associated with species hybridization and horizontal gene transfer, respectively. More recently, implicit evolutionary networks, such as split graphs and minimal lateral networks, have been used to account for reticulate evolution in linguistics. Results: Striking parallels existing between the evolution of species and natural languages allowed us to apply three computational biology methods for reconstruction of phylogenetic networks to model the evolution of IE languages. We show how the transfer of methods between the two disciplines can be achieved, making necessary methodological adaptations. Considering basic vocabulary data from the well-known Dyen's lexical database, which contains word forms in 84 IE languages for the meanings of a 200-meaning Swadesh list, we adapt a recently developed computational biology algorithm for building explicit hybridization networks to study the evolution of IE languages and compare our findings to the results provided by the split graph and galled network methods. Conclusion: We conclude that explicit phylogenetic networks can be successfully used to identify donors and recipients of lexical material as well as the degree of influence of each donor language on the corresponding recipient languages. We show that our algorithm is well suited to detect reticulate relationships among languages, and present some historical and linguistic justification for the results obtained. Our findings could be further refined if relevant syntactic, phonological and morphological data could be analyzed along with the available lexical data.

Language, 2004

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In this paper we examine the usefulness of two classes of algorithms Distance Methods, Discrete Character Methods (Felsenstein and Felsenstein 2003) widely used in genetics, for predicting the family relationships among a set of related languages and therefore, diachronic language change. Applying these algorithms to the data on the numbers of shared cognateswith-change and changed as well as unchanged cognates for a group of six languages belonging to a Dravidian language sub-family given in , we observed that the resultant phylogenetic trees are largely in agreement with the linguistic family tree constructed using the comparative method of reconstruction with only a few minor differences. Furthermore, we studied these minor differences and found that they were cases of genuine ambiguity even for a well-trained historical linguist. We evaluated the trees obtained through our experiments using a well-defined criterion and report the results here. We finally conclude that quantitative methods like the ones we examined are quite useful in predicting family relationships among languages. In addition, we conclude that a modest degree of confidence attached to the intuition that there could indeed exist a parallelism between the processes of linguistic and genetic change is not totally misplaced.

Robbeets, Martine and Mark Hudson (eds.), The Oxford Handbook of Archaeology and Language. Oxford: Oxford University Press.

Linguistic phylogeography is concerned with the reconstruction of the geographical distribution of lineages pertaining to a specific language family (phylogeny). This paper reviews both traditional methods and modern computational ones. The computational methods draw upon information from the range of languages in a phylogeny, inferring origins and patterns of dispersal from current patterns. Some draw upon the structure of phylogenetic trees, some do not. This paper outlines the different methods, recapitulates the results of general evaluations, and identifies the challenges that they face, some of which are shared across the board. As a contribution unique to this paper, hypothetical homelands for all the world's language families as well as their subgroups generated by the new 'minimal distance' method are published here for the first time.

2022

Within linguistics, there is an ongoing debate about whether some language structures remain stable over time, which structures these are and whether they can be used to uncover the relationships between languages. However, there is no consensus on the definition of the term 'stability'. I define 'stability' as a high phylogenetic signal and a low rate of change. I use metric D to measure the phylogenetic signal and Hidden Markov Model to calculate the evolutionary rate for 171 structural features coded for 12 Japonic, 2 Koreanic, 14 Mongolic, 11 Tungusic and 21 Turkic languages. To more deeply investigate the differences in evolutionary dynamics of structural features across areas of grammar, I divide the features into 4 language domains, 13 functional categories and 9 parts of speech. My results suggest that there is a correlation between the phylogenetic signal and evolutionary rate and that, overall, twothirds of the features have a high phylogenetic signal and over a half of the features evolve at a slow rate. Specifically, argument marking (flagging and indexing), derivation and valency appear to be the most stable functional categories, pronouns and nouns the most stable parts of speech, and phonological and morphological levels the most stable language domains.

Historical Linguistics studies language change over time. If a group of languages derives from changes to a common ancestor language (proto-language) then they are said to be related. Whenever there exists a lack of written records for an ancestor language, a relevant question in Historical Linguistics is to determine whether two languages are related. The gold standard for finding these relationships is the Comparative Method. Despite the success of the Comparative Method in finding language relationships, it suffers from at least two limitations. First, the Comparative Method involves the manual comparison of various features from a group of languages. Second, the Comparative Method doesn't provide a numerical measure of evidence for how much the database under consideration corroborates an hypothesis. Given the above limitations, the field of Computational Historical Linguistics is presented as a complement to the Comparative Method. This field has experienced a recent expansion with the adaptation of methods from biological phylogenetics. Nevertheless, there is debate whether the evolutionary models used in phylogenetics also incorporate valid linguistical assumptions. In this thesis, I propose a new (probability) model for the evolution of the phonology of languages. A relevant innovation of this model is that it captures the regularity of sound changes. I also describe the software that I created in order to assist in incorporating into the model a linguist's expert knowledge. I show that the knowledge obtained in this way agrees with qualitative statements known to linguists. Finally, I present a new algorithm used to compute the probability of linguistic hypotheses regarding language relationships and the occurence of regular sound changes. The main problem that this algorithm overcomes is that it efficiently explores the possible regular sound changes, mutations in languages that simultaneously affect several words. In order to overcome this challange, I present a new variant of Nested Sequential Monte Carlo that is used to explore the large space of language relationships and regular sound changes. To the best of my knowledge, this is the first algorithm that can perform joint inference on regular sound changes and evolutionary trees. I show that this algorithm is a special case of Sequential Importance Resampling.

The Oxford Guide to the Transeurasian Languages, 2020

This chapter provides an overview of the typological features of the Transeurasian (Turkic, Mongolic, Tungusic, Japonic, Koreanic) languages, including brief descriptions of the phonology and morphosyntax of these languages. Through the application of phylogenetic comparative methods, a set of structural features with a high phylogenetic signal is delimited. These features can be assumed to be genealogically stable. The trees achieved by Bayesian tree-sampling based on all 226 features are compared with those derived via the 97 structural features with a high phylogenetic signal and the conclusion reached is that the data set with presumably stable structural features does not provide a tree that is compatible with the language history assumed by classical historical linguists. Neither the full nor the reduced feature set provides a reliable internal classification of the Turkic, Mongolic, Tungusic, and Japonic language families.

2012

Similarities between languages can be due to 1) homoplasies because of a limited design space, 2) common ancestry, and 3) contact-induced convergence. Typological or structural features cannot prove genealogy, but they can provide historical signals that are due to common ancestry or contact (or both).