Quantitative comparative linguistics is the use of quantitative analysis as applied to comparative linguistics. Examples include the statistical fields of lexicostatistics and glottochronology, and the borrowing of phylogenetics from biology.
History Statistical methods have been used for the purpose of quantitative analysis in comparative linguistics for more than a century. During the 1950s, the Swadesh list emerged: a standardised set of lexical concepts found in most languages, as words or phrases, that allow two or more languages to be compared and contrasted empirically.
Probably the first published quantitative historical linguistics study was by Sapir in 1916, while Kroeber and Chretien in 1937 investigated nine Indo-European (IE) languages using 74 morphological and phonological features (extended in 1939 by the inclusion of Hittite). Ross in 1950 carried out an investigation into the theoretical basis for such studies. Swadesh, using word lists, developed lexicostatistics and glottochronology in a series of papers published in the early 1950s but these methods were widely criticised though some of the criticisms were seen as unjustified by other scholars. Embleton published a book on "Statistics in Historical Linguistics" in 1986 which reviewed previous work and extended the glottochronological method. Dyen, Kruskal and Black carried out a study of the lexicostatistical method on a large IE database in 1992.
During the 1990s, there was renewed interest in the topic, based on the application of methods of computational phylogenetics and cladistics. Such projects often involved collaboration by linguistic scholars, and colleagues with expertise in information science and/or biological anthropology. These projects often sought to arrive at an optimal phylogenetic tree (or network), to represent a hypothesis about the evolutionary ancestry and perhaps its language contacts. Pioneers in these methods included the founders of CPHL: computational phylogenetics in historical linguistics (CPHL project): Donald Ringe, Tandy Warnow, Luay Nakhleh and Steven N. Evans.
In the mid-1990s a group at Pennsylvania University computerised the comparative method and used a different IE database with 20 ancient languages. In the biological field several software programs were then developed which could have application to historical linguistics. In particular a group at the University of Auckland developed a method that gave controversially old dates for IE languages.Initially announced in A conference on "Time-depth in Historical Linguistics" was held in August 1999 at which many applications of quantitative methods were discussed.Published by Renfrew, McMahon and Trask in 2000 Subsequently many papers have been published on studies of various language groups as well as comparisons of the methods. The proceedings of a 2004 conference, Phylogenetic Methods and the Prehistory of Languages were published in 2006, edited by Peter Forster and Colin Renfrew.
Studied language families Computational phylogenetic analyses have been performed for:
Indo-European languages: Bouckaert (2012) Uralic languages: Honkola (2013) Turkic languages: Hruschka (2014) Dravidian languages: Kolipakam (2018) Austroasiatic languages: Sidwell (2015)Sidwell, Paul. 2015. A comprehensive phylogenetic analysis of the Austroasiatic languages. Presented at Diversity Linguistics: Retrospect and Prospect, 1–3 May 2015 (Leipzig, Germany), Closing conference of the Department of Linguistics at the Max Planck Institute for Evolutionary Anthropology. Austronesian languages: Gray (2009) Pama-Nyungan languages: Bowern & Atkinson (2012),Bowern, Claire and Atkinson, Quentin, 2012. Computational Phylogenetics and the Internal Structure of Pama-Nyungan. Language, Vol. 88, 817-845. Bouckaert, Bowern and Atkinson (2018) Bantu languages: Currie (2013), Grollemund (2015) Semitic languages: Kitchen (2009) Dené–Yeniseian languages: Sicoli & Holton (2014) Uto-Aztecan languages: Wheeler & Whiteley (2014) Mayan languages: Atkinson (2006)Atkinson, Q. D. (2006). From Species to Languages – a phylogenetic approach to human history. PhD thesis, University of Auckland, Auckland. Arawakan languages: Walker & Ribeiro (2011) Tupi-Guarani languages: Michael (2015)Michael, Lev, Natalia Chousou-Polydouri, Keith Bartolomei, Erin Donnelly, Vivian Wauters, Sérgio Meira, Zachary O'Hagan. 2015. A Bayesian Phylogenetic Classification of Tupí-Guaraní. LIAMES 15(2):193-221. Sino-Tibetan languages: Zhang et al. (2019), Sagart et al. (2019)
Background The standard method for assessing language relationships has been the comparative method. However this has a number of limitations. Not all linguistic material is suitable as input and there are issues of the linguistic levels on which the method operates. The reconstructed languages are idealized and different scholars can produce different results. Language family trees are often used in conjunction with the method and "borrowings" must be excluded from the data, which is difficult when borrowing is within a family. It is often claimed that the method is limited in the time depth over which it can operate. The method is difficult to apply and there is no independent test. Thus alternative methods have been sought that have a formalised method, quantify the relationships and can be tested.
A goal of comparative historical linguistics is to identify instances of genetic relatedness amongst languages. The steps in quantitative analysis are (i) to devise a procedure based on theoretical grounds, on a particular model or on past experience, etc. (ii) to verify the procedure by applying it to some data where there exists a large body of linguistic opinion for comparison (this may lead to a revision of the procedure of stage (i) or at the extreme of its total abandonment) (iii) to apply the procedure to data where linguistic opinions have not yet been produced, have not yet been firmly established or perhaps are even in conflict.
Applying phylogenetic methods to languages is a multi-stage process: (a) the encoding stage — getting from real languages to some expression of the relationships between them in the form of numerical or state data, so that those data can then be used as input to phylogenetic methods (b) the representation stage — applying phylogenetic methods to extract from those numerical and/or state data a signal that is converted into some useful form of representation, usually two dimensional graphical ones such as trees or networks, which synthesise and "collapse" what are often highly complex multi dimensional relationships in the signal (c) the interpretation stage - assessing those tree and network representations to extract from them what they actually mean for real languages and their relationships through time.
Types of trees and networks
An output of a quantitative historical linguistic analysis is normally a tree or a network diagram. This allows summary visualisation of the output data but is not the complete result. A tree is a connected acyclic graph, consisting of a set of vertices (also known as "nodes") and a set of edges ("branches") each of which connects a pair of vertices. An internal node represents a linguistic ancestor in a phylogenic tree or network. Each language is represented by a path, the paths showing the different states as it evolves. There is only one path between every pair of vertices. Unrooted trees plot the relationship between the input data without assumptions regarding their descent. A rooted tree explicitly identifies a common ancestor, often by specifying a direction of evolution or by including an "outgroup" that is known to be only distantly related to the set of languages being classified. Most trees are binary, that is a parent has two children. A tree can always be produced even though it is not always appropriate. A different sort of tree is that only based on language similarities / differences. In this case the internal nodes of the graph do not represent ancestors but are introduced to represent the conflict between the different splits ("bipartitions") in the data analysis. The "phenetic distance" is the sum of the weights (often represented as lengths) along the path between languages. Sometimes an additional assumption is made that these internal nodes do represent ancestors.
When languages converge, usually with word adoption ("borrowing"), a network model is more appropriate. There will be additional edges to reflect the dual parentage of a language. These edges will be bidirectional if both languages borrow from one another. A tree is thus a simple network, however there are many other types of network. A phylogentic network is one where the taxa are represented by nodes and their evolutionary relationships are represented by branches. Another type is that based on splits, and is a combinatorial generalisation of the split tree. A given set of splits can have more than one representation thus internal nodes may not be ancestors and are only an "implicit" representation of evolutionary history as distinct from the "explicit" representation of phylogenetic networks. In a splits network the phrenetic distance is that of the shortest path between two languages. A further type is the reticular network which shows incompatibilities (due to for example to contact) as reticulations and its internal nodes do represent ancestors. A network may also be constructed by adding contact edges to a tree. The last main type is the consensus network formed from trees. These trees may be as a result of bootstrap analysis or samples from a posterior distribution.
Language change Change happens continually to languages, but not usually at a constant rate, with its cumulative effect producing splits into dialects, languages and language families. It is generally thought that morphology changes slowest and phonology the quickest. As change happens, less and less evidence of the original language remains. Finally there could be loss of any evidence of relatedness. Changes of one type may not affect other types, for example sound changes do not affect cognacy. Unlike biology, it cannot be assumed that languages all have a common origin and establishing relatedness is necessary. In modelling it is often assumed for simplicity that the characters change independently but this may not be the case. Besides borrowing, there can also be semantic shifts and polymorphism.
Databases Swadesh originally published a 200 word list but later refined it into a 100 word one. A commonly used IE database is that by Dyen, Kruskal and Black which contains data for 95 languages, though the original is known to contain a few errors. Besides the raw data it also contains cognacy judgements.At http://www.idc.upenn.edu The database of Ringe, Warnow and Taylor has information on 24 IE languages, with 22 phonological characters, 15 morphological characters and 333 lexical characters. Gray and Atkinson used a database of 87 languages with 2449 lexical items, based on the Dyen set with the addition of three ancient languages. They incorporated the cognacy judgements of a number of scholars. Other databases have been drawn up for African, Australian and Andean language families, amongst others.
Coding of the data may be in binary form or in multistate form. The former is often used but does result in a bias. It has been claimed that there is a constant scale factor between the two coding methods, and that allowance can be made for this. However, another study suggests that the topology may change
Word lists