An automated framework for fast cognate detection and Bayesian phylogenetic inference in computational historical linguistics

We present a fully automated workflow for phylogenetic reconstruction on large datasets, consisting of two novel methods, one for fast detection of cognates and one for fast Bayesian phylogenetic inference. Our results show that the methods take less than a few minutes to process language families that have so far required large amounts of time and computational power. Moreover, the cognates and the trees inferred from the method are quite close, both to gold standard cognate judgments and to expert language family trees. Given its speed and ease of application, our framework is specifically useful for the exploration of very large datasets in historical linguistics

Information-theoretic causal inference of lexical flow

This volume seeks to infer large phylogenetic networks from phonetically encoded lexical data and contribute in this way to the historical study of language varieties. The technical step that enables progress in this case is the use of causal inference algorithms. Sample sets of words from language varieties are preprocessed into automatically inferred cognate sets, and then modeled as information-theoretic variables based on an intuitive measure of cognate overlap. Causal inference is then applied to these variables in order to determine the existence and direction of influence among the varieties. The directed arcs in the resulting graph structures can be interpreted as reflecting the existence and directionality of lexical flow, a unified model which subsumes inheritance and borrowing as the two main ways of transmission that shape the basic lexicon of languages. A flow-based separation criterion and domain-specific directionality detection criteria are developed to make existing causal inference algorithms more robust against imperfect cognacy data, giving rise to two new algorithms. The Phylogenetic Lexical Flow Inference (PLFI) algorithm requires lexical features of proto-languages to be reconstructed in advance, but yields fully general phylogenetic networks, whereas the more complex Contact Lexical Flow Inference (CLFI) algorithm treats proto-languages as hidden common causes, and only returns hypotheses of historical contact situations between attested languages. The algorithms are evaluated both against a large lexical database of Northern Eurasia spanning many language families, and against simulated data generated by a new model of language contact that builds on the opening and closing of directional contact channels as primary evolutionary events. The algorithms are found to infer the existence of contacts very reliably, whereas the inference of directionality remains difficult. This currently limits the new algorithms to a role as exploratory tools for quickly detecting salient patterns in large lexical datasets, but it should soon be possible for the framework to be enhanced e.g. by confidence values for each directionality decision

Are Automatic Methods for Cognate Detection Good Enough for Phylogenetic Reconstruction in Historical Linguistics?

We evaluate the performance of state-of-the-art algorithms for automatic cognate detection by comparing how useful automatically inferred cognates are for the task of phylogenetic inference compared to classical manually annotated cognate sets. Our findings suggest that phylogenies inferred from automated cog- nate sets come close to phylogenies inferred from expert-annotated ones, although on average, the latter are still superior. We con- clude that future work on phylogenetic reconstruction can profit much from automatic cognate detection. Especially where scholars are merely interested in exploring the bigger picture of a language family’s phylogeny, algorithms for automatic cognate detection are a useful complement for current research on language phylogenies

A computer-assisted pproach to the comparison of mainland southeast Asian languages

This cumulative thesis is based on three separate projects based on a computer-assisted language comparison (CALC) framework to address common obstacles to studying the history of Mainland Southeast Asian (MSEA) languages, such as sparse and non-standardized lexical data, as well as an inadequate method of cognate judgments, and to provide caveats to scholars who will use Bayesian phylogenetic analysis. The first project provides a format that standardizes the sound inventories, regulates language labels, and clarifies lexical items. This standardized format allows us to merge various forms of raw data. The format also summarizes information to assist linguists in researching the relatedness among words and inferring relationships among languages. The second project focuses on increasing the transparency of lexical data and cognate judg- ments with regard to compound words. The method enables the annotation of each part of a word with semantic meanings and syntactic features. In addition, four different conversion methods were developed to convert morpheme cognates into word cognates for input into the Bayesian phylogenetic analysis. The third project applies the methods used in the first project to create a workflow by merging linguistic data sets and inferring a language tree using a Bayesian phylogenetic algorithm. Further- more, the project addresses the importance of integrating cross-disciplinary studies into historical linguistic research. Finally, the methods we proposed for managing lexical data for MSEA languages are discussed and summarized in six perspectives. The work can be seen as a milestone in reconstructing human prehistory in an area that has high linguistic and cultural diversity

Information-theoretic causal inference of lexical flow

This volume seeks to infer large phylogenetic networks from phonetically encoded lexical data and contribute in this way to the historical study of language varieties. The technical step that enables progress in this case is the use of causal inference algorithms. Sample sets of words from language varieties are preprocessed into automatically inferred cognate sets, and then modeled as information-theoretic variables based on an intuitive measure of cognate overlap. Causal inference is then applied to these variables in order to determine the existence and direction of influence among the varieties. The directed arcs in the resulting graph structures can be interpreted as reflecting the existence and directionality of lexical flow, a unified model which subsumes inheritance and borrowing as the two main ways of transmission that shape the basic lexicon of languages

Trimming Phonetic Alignments Improves the Inference of Sound Correspondence Patterns from Multilingual Wordlists

Sound correspondence patterns form the basis of cognate detection and phonological reconstruction in historical language comparison. Methods for the automatic inference of correspondence patterns from phonetically aligned cognate sets have been proposed, but their application to multilingual wordlists requires extremely well annotated datasets. Since annotation is tedious and time consuming, it would be desirable to find ways to improve aligned cognate data automatically. Taking inspiration from trimming techniques in evolutionary biology, which improve alignments by excluding problematic sites, we propose a workflow that trims phonetic alignments in comparative linguistics prior to the inference of correspondence patterns. Testing these techniques on a large standardized collection of ten datasets with expert annotations from different language families, we find that the best trimming technique substantially improves the overall consistency of the alignments. The results show a clear increase in the proportion of frequent correspondence patterns and words exhibiting regular cognate relations.Comment: The paper was accepted at the SIGTYP workshop 2023 co-located with EAC

Automatic Loanword Identification Using Tree Reconciliation

Die Verwendung von computerbasierten Methoden in der Historischen Linguistik stieg in den letzten Jahren stetig an. Phylogenetische Methoden, welche zur Bestimmung der Evolutionsgeschichte und Verwandtschaftsgraden zwischen Organismen entwickelt wurden, erhielten Einzug in die Historische Linguistik. Die Verfügbarkeit von maschinenlesbaren Daten förderten deren Anpassung und Weiterentwicklung. Während einige Algorithmen zur Rekonstruktion der sprachlichen Evolutionsgeschichte übernommen wurden, wurde den Methoden für horizontalen Transfer kaum Beachtung geschenkt. Angelehnt an die Parallele zwischen horizontalem Gentransfer und Entlehnung, werden in dieser Arbeit phylogenetische Methoden zur Erkennung von horizontalem Gentransfer für die Identifikation von Lehnwörtern verwendet. Die Algorithmen für horizontalen Gentransfer basieren auf dem Vergleich zweier phylogenetischer Bäume. In der Linguistik bildet der Sprachbaum die Sprachgeschichte ab, während ein Konzeptbaum die Evolutionsgeschichte einzelner Wörter repräsentiert. Die Rekonstruktion eines Sprachbaumes ist wissenschaftlich fundiert, wohingegen die Rekonstruktion von Konzeptbäumen bisher wenig erforscht wurde. Eine erhebliche Innovation dieser Arbeit ist die Einführung verschiedener Methoden zur Rekonstruktion von stabilen Konzeptbäumen. Da die Algorithmen zur Erkennung von horizontalem Transfer auf einem Baumvergleich basieren, deuten die Unterschiede zwischen einem Sprachbaum und einem Konzeptbaum auf Lehnwörter innerhalb der Daten hin. Daher wird sowohl die Methodik, als auch ein geeigneter Algorithmus in einem linguistischen Kontext eingeführt. Die Ergebnisse der Lehnworterkennung werden mithilfe eines neu entwickelten Goldstandards evaluiert und mit drei weiteren Algorithmen aus der Historischen Computerlinguistik verglichen. Ziel der Arbeit ist zu erläutern, inwieweit Algorithmen basierend auf dem Vergleich zweier Bäume für die automatische Lehnworterkennung verwendet und in welchem Umfang Lehnwörter erfolgreich innerhalb der Daten bestimmt werden können. Die Identifikation von Lehnwörtern trägt zu einem tieferen Verständnis von Sprachkontakt und den unterschiedlichen Arten von Lehnwörtern bei. Daher ist die Adaption von phylogenetischen Methoden nicht nur lohnenswert für die Bestimmungen von Entlehnungen, sondern dient auch als Basis für weitere, detailliertere Analysen auf den Gebieten der automatischen Lehnworterkennung und Kontaktlinguistik.The use of computational methods in historical linguistics increased during the last years. Phylogenetic methods, which explore the evolutionary history and relationships among organisms, found their way into historical linguistics. The availability of machine-readable data accelerated their adaptation and development. While some methods addressing the evolution of languages are integrated into linguistics, scarcely any attention has been paid to methods analyzing horizontal transmission. Inspired by the parallel between horizontal gene transfer and borrowing, this thesis aims at adapting horizontal transfer methods into computational historical linguistics to identify borrowing scenarios along with the transferred loanwords. Computational methods modeling horizontal transfer are based on the framework of tree reconciliation. The methods attempt to detect horizontal transfer by fitting the evolutionary history of words to the evolution of their corresponding languages, both represented in phylogenetic trees. The discordance between the two evolutionary scenarios indicates the influence of loanwords due to language contact. The tree reconciliation framework is introduced in a linguistic setting along with an appropriate algorithm, which is applied to linguistic trees to detect loanwords. While the reconstruction of language trees is scientifically substantiated, little research has so far be done on the reconstruction of concept trees, representing the words’ histories. One major innovation of this thesis is the introduction of various methods to reconstruct reliable concept trees and determine their stability in order to achieve reasonable results in terms of loanword detection. The results of the tree reconciliation are evaluated against a newly developed gold standard and compared to three methods established for the task of language contact detection in computational historical linguistics. The main aim of this thesis is to clarify the purpose of tree reconciliation methods in linguistics. The following analyses should give insights to which degree the direct transfer of phylogenetic methods into the field of linguistics is fruitful and can be used to discover borrowings along with the transferred loanwords. The identification of loanwords is a first step into the direction of a deeper understanding of contact scenarios and possible types of loanwords present in linguistic data. The adaptation of phylogenetic methods is not only worthwhile to shed light on detailed horizontal transmissions, but serves as basis for further, more detailed analyses in the field of contact linguistics

Algorithmic advancements in Computational Historical Linguistics

Computergestützte Methoden in der historischen Linguistik haben in den letzten Jahren einen großen Aufschwung erlebt. Die wachsende Verfügbarkeit maschinenlesbarer Daten förderten diese Entwicklung ebenso wie die zunehmende Leistungsfähigkeit von Computern. Die in dieser Forschung verwendeten Berechnungsmethoden stammen aus verschiedenen wissenschaftlichen Disziplinen, wobei Methoden aus der Bioinformatik sicherlich die Initialzündung gaben. Diese Arbeit, die sich von Fortschritten in angrenzenden Gebieten inspirieren lässt, zielt darauf ab, die bestehenden Berechnungsmethoden in verschiedenen Bereichen der computergestützten historischen Linguistik zu verbessern. Mit Hilfe von Fortschritten aus der Forschung aus dem maschinellen Lernen und der Computerlinguistik wird hier eine neue Trainingsmethode für Algorithmen zur Kognatenerkennung vorgestellt. Diese Methode erreicht an vielen Stellen die besten Ergebnisse im Bereich der Kognatenerkennung. Außerdem kann das neue Trainingsschema die Rechenzeit erheblich verbessern. Ausgehend von diesen Ergebnissen wird eine neue Kombination von Methoden der Bioinformatik und der historischen Linguistik entwickelt. Durch die Definition eines expliziten Modells der Lautevolution wird der Begriff der evolutionären Zeit in die Kognatenerkennung mit einbezogen. Die sich daraus ergebenden posterioren Verteilungen werden verwendet, um das Modell anhand einer standardmäßigen Kognatenerkennung zu evaluieren. Eine weitere klassische Problemstellung in der pyhlogenetischen Forschung ist die Inferenz eines Baumes. Aktuelle Methoden, die den ``quasi-industriestandard'' bilden, verwenden den klassischen Metropolis-Hastings-Algorithmus. Allerdings ist bekannt, dass dieser Algorithmus für hochdimensionale und korrelierte Daten vergleichsweise ineffizient ist. Um dieses Problem zu beheben, wird im letzten Kapitel ein Algorithmus vorgestellt, der die Hamilton'sche Dynamik verwendet.The use of computational methods in historical linguistics has seen a large boost in recent years. An increasing availability of machine readable data and the growing power of computers fostered this development. While the computational methods which are used in this research stem from different scientific disciplines, a lot of tools from computational biology have found their way into this research. Drawing inspiration from advancements in related fields, this thesis aims at improving existing computational methods in different disciplines of computational historical linguistics. Using advancements from machine learning and natural language processing research, I present an updated training regime for cognate detection algorithms. Besides achieving state of the art performance in a cognate clustering task, the updated training scheme considerably improved computation time. Following up on these results, I develop a novel combination of tools from bioinformatics and historical linguistics is developed. By defining an explicit model of sound evolution, I include the notion of evolutionary time into a cognate detection task. The resulting posterior distributions are used to evaluate the model on a standard cognate detection task. A standard problem in phylogenetic research is the inference of a tree. Current quasi "industry-standard" methods use the classical Metropolis-Hastings algorithm. However, this algorithm is known to be rather inefficient for high dimensional and correlated data. To solve this problem, I present an algorithm which uses Hamiltonian dynamics in the last chapter

Computational Historical Linguistics

In the course, I give a basic introduction into some of the recent developments in the field of computational historical linguistics. While this field is predominantly represented by phylogenetic approaches with whom scholars try to infer phylogenetic trees from different kinds of language data, the approach taken here is much broader, concentrating specifically on the prerequisites needed in order to get one’s data into the shape to carry out phylogenetic analyses. As a result, we will concentrate on topics such as automated phonetic alignments, automated cognate detection, the handling of semantic shift, and the modeling of word formation in comparative wordlists. A major goal of the course is to emphasize the importance of computer-assisted — as opposed to computer-based — approaches, which acknowledge the importance of qualitative work in historical language comparison. The course will be accompanied by code examples which participants can try to replicate on their computers