Johann-Mattis List

Automated dating of the world’s language families based on lexical similarity

This paper describes a computerized alternative to glottochronology for estimating elapsed time since parent languages diverged into daughter languages. The method, developed by the Automated Similarity Judgment Program (ASJP) consortium, is different from glottochronology in four major respects: (1) it is automated and thus is more objective, (2) it applies a uniform analytical approach to a single database of worldwide languages, (3) it is based on lexical similarity as determined from Levenshtein (edit) distances rather than on cognate percentages, and (4) it provides a formula for date calculation that mathematically recognizes the lexical heterogeneity of individual languages, including parent languages just before their breakup into daughter languages. Automated judgments of lexical similarity for groups of related languages are calibrated with historical, epigraphic, and archaeological divergence dates for 52 language groups. The discrepancies between estimated and calibration dates are found to be on average 29% as large as the estimated dates themselves, a figure that does not differ significantly among language families. As a resource for further research that may require dates of known level of accuracy, we offer a list of ASJP time depths for nearly all the world’s recognized language families and for many subfamilies.

Networks uncover hidden lexical borrowing in Indo-European language evolution

Language evolution is traditionally described in terms of family trees with ancestral languages splitting into descendent languages. However, it has long been recognized that language evolution also entails horizontal components, most commonly through lexical borrowing. For example, the English language was heavily influenced by Old Norse and Old French; eight per cent of its basic vocabulary is borrowed. Borrowing is a distinctly non-tree-like process—akin to horizontal gene transfer in genome evolution—that cannot be recovered by phylogenetic trees. Here, we infer the frequency of hidden borrowing among 2346 cognates (etymologically related words) of basic vocabulary distributed across 84 Indo-European languages. The dataset includes 124 (5%) known borrowings. Applying the uniformitarian principle to inventory dynamics in past and present basic vocabularies, we find that 1373 (61%) of the cognates have been affected by borrowing during their history. Our approach correctly identified 117 (94%) known borrowings. Reconstructed phylogenetic networks that capture both vertical and horizontal components of evolutionary history reveal that, on average, eight per cent of the words of basic vocabulary in each Indo-European language were involved in borrowing during evolution. Basic vocabulary is often assumed to be relatively resistant to borrowing. Our results indicate that the impact of borrowing is far more widespread than previously thought.

Networks of lexical borrowing and lateral gene transfer in language and genome evolution

Like biological species, languages change over time. As noted by Darwin, there are many parallels between language evolution and biological evolution. Insights into these parallels have also undergone change in the past 150 years. Just like genes, words change over time, and language evolution can be likened to genome evolution accordingly, but what kind of evolution? There are fundamental differences between eukaryotic and prokaryotic evolution. In the former, natural variation entails the gradual accumulation of minor mutations in alleles. In the latter, lateral gene transfer is an integral mechanism of natural variation. The study of language evolution using biological methods has attracted much interest of late, most approaches focusing on language tree construction. These approaches may underestimate the important role that borrowing plays in language evolution. Network approaches that were originally designed to study lateral gene transfer may provide more realistic insights into the complexities of language evolution.

SCA: phonetic alignment based on sound classes

In this paper I present the most recent version of the SCA method for pairwise and multiple alignment analyses. In contrast to previously proposed alignment methods, SCA is based on a novel framework of sequence alignment which combines new approaches to sequence modeling in historical linguistics with recent developments in computational biology. In contrast to earlier versions of SCA [1,2] the new version comes along with a couple of modifications that significantly improve the performance and the application range of the algorithm: A new sound class model was defined which works well on highly divergent sequences, the algorithm for pairwise alignment was modified to be sensitive to secondary sequence structures such as syllable boundaries, and an algorithm for the pre-processing of the data in multiple alignment analyses [3] was included to cope for the bias resulting from progressive alignment analyses. In order to test the method, a new gold standard for pairwise and multiple alignment analyses was created which consists of 45 947 sequences covering a total of 435 different taxa belonging to six different language families.

Using Phylogenetic Networks to Model Chinese Dialect History

The idea that language history is best visualized by a branching tree has been controversially discussed in the linguistic world and many alternative theories have been proposed. The reluctance of many scholars to accept the tree as the natural metaphor for language history was due to conflicting signals in linguistic data: many resemblances would simply not point to a unique tree. Despite these observations, the majority of automatic approaches applied to language data has been based on the tree model, while network approaches have rarely been applied. Due to the specific sociolinguistic situation in China, where very divergent varieties have been developing under the roof of a common culture and writing system, the history of the Chinese dialects is complex and intertwined. They are therefore a good test case for methods which no longer take the family tree as their primary model. Here we use a network approach to study the lexical history of 40 Chinese dialects. In contrast to previous approaches, our method is character-based and captures both vertical and horizontal aspects of language history. According to our results, the majority of characters in our data (about 54%) cannot be readily explained with the help of a given tree model. The borrowing events inferred by our method do not only reflect general uncertainties of Chinese dialect classification, they also reveal the strong influence of the standard language on Chinese dialect history.

Using Sequence Similarity Networks to Identify Partial Cognates in Multilingual Wordlists.

Increasing amounts of digital data in historical linguistics necessitate the development of automatic methods for the detection of cognate words across languages. Recently developed methods work well on language families with moderate time depths, but they are not capable of identifying cognate morphemes in words which are only partially related. Partial cognacy, however, is a frequently recurring phenomenon, especially in language families with productive derivational morphology. This paper presents a pilot approach for partial cognate detection in which networks are used to represent similarities between word parts and cognate morphemes are identified with help of state-of-theart algorithms for network partitioning. The approach is tested on a newly created benchmark dataset with data from three sub-branches of Sino-Tibetan and yields very promising results, outperforming all algorithms which are not sensible to partial cognacy.

Unity and disunity in evolutionary sciences: process-based analogies open common research avenues for biology and linguistics.

BackgroundFor a long time biologists and linguists have been noticing surprising similarities between the evolution of life forms and languages. Most of the proposed analogies have been rejected. Some, however, have persisted, and some even turned out to be fruitful, inspiring the transfer of methods and models between biology and linguistics up to today. Most proposed analogies were based on a comparison of the research objects rather than the processes that shaped their evolution. Focusing on process-based analogies, however, has the advantage of minimizing the risk of overstating similarities, while at the same time reflecting the common strategy to use processes to explain the evolution of complexity in both fields.ResultsWe compared important evolutionary processes in biology and linguistics and identified processes specific to only one of the two disciplines as well as processes which seem to be analogous, potentially reflecting core evolutionary processes. These new process-based analogies support novel methodological transfer, expanding the application range of biological methods to the field of historical linguistics. We illustrate this by showing (i) how methods dealing with incomplete lineage sorting offer an introgression-free framework to analyze highly mosaic word distributions across languages; (ii) how sequence similarity networks can be used to identify composite and borrowed words across different languages; (iii) how research on partial homology can inspire new methods and models in both fields; and (iv) how constructive neutral evolution provides an original framework for analyzing convergent evolution in languages resulting from common descent (Sapir’s drift).ConclusionsApart from new analogies between evolutionary processes, we also identified processes which are specific to either biology or linguistics. This shows that general evolution cannot be studied from within one discipline alone. In order to get a full picture of evolution, biologists and linguists need to complement their studies, trying to identify cross-disciplinary and discipline-specific evolutionary processes. The fact that we found many process-based analogies favoring transfer from biology to linguistics further shows that certain biological methods and models have a broader scope than previously recognized. This opens fruitful paths for collaboration between the two disciplines.ReviewersThis article was reviewed by W. Ford Doolittle and Eugene V. Koonin.

Glottochronology as a Heuristic for Genealogical Language Relationships

Abstract This paper applies a computerized method related to that of glottochronology and addresses the question whether such a method is useful as a heuristic for identifying deep genealogical relations among languages. We first measure lexical similarities for pairs of language families that are normally assumed to be unrelated, using a modification of the Levenshtein distance as our similarity measure. We then go on to study how the similarities are statistically distributed. The average similarity is slightly greater than zero, suggesting a small effect of sound symbolism. The upper tail of the distribution extends to similarities comparable to what is typically found for well-established families or highest-order subgroups of old families, but the pairs of unrelated families with the highest similarities contain only a few languages. We conclude that the method may work as a useful heuristic, provided that the number of languages compared is taken into account.

Using ancestral state reconstruction methods for onomasiological reconstruction in multilingual word lists

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 Likelihood 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.

Challenges of annotation and analysis in computer-assisted language comparison : A case study on Burmish languages

Abstract The use of computational methods in comparative linguistics is growing in popularity. The increasing deployment of such methods draws into focus those areas in which they remain inadequate as well as those areas where classical approaches to language comparison are untransparent and inconsistent. In this paper we illustrate specific challenges which both computational and classical approaches encounter when studying South-East Asian languages. With the help of data from the Burmish language family we point to the challenges resulting from missing annotation standards and insufficient methods for analysis and we illustrate how to tackle these problems within a computer-assisted framework in which computational approaches are used to pre-analyse the data while linguists attend to the detailed analyses.