Statistical proof of language relatedness (Open problems in computational diversity linguistics 7)

The more I advance with the problems I want to present during this year, the more I have to admit to myself, sometimes, that the problem I planned to present is so difficult that I find it even hard to simply present the state-of-the-art. The problem of this month, problem number 7 in my list, is such an example — proving that two or more languages are "genetically related", as historical linguists (incorrectly) tend to say, is not only hard, it is also extremely difficult even to summarize the topic properly.

Typically, colleagues start with the famous but also not very helpful quote of Sir William Jones, who delivered a report to the British Indian Company, thereby mentioning that there might be a deeper relationship between Sanskrit and some European languages (like Greek and Latin). The article, titled The third anniversary discourse, delivered 2 February, 1786, by the president (published in 1798) has by now been quoted so many times that it is better to avoid quoting it another time (but you will find the full quote with references in my reference library.

In contrast to later scholars like Jacob Grimm and Rasmus Rask, however, Jones does not prove anything, he just states an opinion. The reason why scholars like to quote him, is that he seems to talk about probability, since he mentions the impossibility that the resemblances between the languages he observed could have arisen by chance. Since a great deal of the discussion about language relationship centers around the question how chance could be controlled for, it is a welcome quote from the olden times to be used when writing a paper on statistics or quantitative methods. But this does not necessarily mean that Jones really knew what he was writing about, as one can read in detail in the very interesting book by Campbell and Poser (2008), which deals at length with the supposedly overrated role that William Jones played in the early history of historical linguistics.

Macro Families

Returning to the topic at hand. The regularity of sound change and the possibility to prove language relationship in some cases was an unexpected detection of some linguists during the early 19th century, but what many linguists have been dreaming about since is to expand their methods to such a degree that even deeper relationships could be proven. While the evidence for the relationship of the core Indo-European languages was more or less convincing by itself (as rightfully pointed out by Nichols 1996), scholars have proposed many suggestions of relationship, many of which are no longer followed by the communis opinio. Among these long-range proposals for deep phylogenetic relations are theories that further unite fully established language families, proposing large macro-families — such as Nostratic (uniting Semitic, Indo-European, and many more, depending on the respective version), Altaic (uniting Turkic, Mongolic, Tungusic, Japanese, and Korean, etc.), or Dene-Caucasian (uniting Sino-Tibetan, North Caucasian, and Na-Dene), which span incredibly large areas on earth.

Given that it the majority of scholars mistrust these new and risky proposals, and that even scholars who work in the field of long-range comparison often disagree with each other, it is not surprising that at least some linguists became interested in the question of how long-range relationship could be proven in the end. One of the first attempts in this regard was presented by Aharon Dolgopolsky, a convinced Nostratic linguist, who presented a first, very interesting, heuristic procedure to determine deep cognates and deep language relationships, by breaking sounds down to more abstract classes, in order to address the problem that words often do no longer look similar due to sound change (Dolgopolsky 1964).

Why it is hard to prove language relationship

Dolgopolsky did not use any statistics to prove his approach, but he emphasized the probabilistic aspect of his endeavor, and derived his "consonant classes" or "sound classes" as well as his very short list of stable concepts from the empirical investigation of a large corpus. The core of his approach, to fix a list of semantic items, presumably "stable" (i.e. slowly changing with respect to semantic shift), and to reduce the complexity of phonetic transcriptions to a core meta-alphabet, has been the basis of many follow-up studies that follow an explicitly quantitative (or statistic) approach.

As of now, most scholars, be they classical or computational, agree that the first stage of historical language comparison consists of the proof that the languages one wants to investigate are, indeed, historically related to each other (for the underlying workflow of historical language comparison, see Ross and Durie). In a blogpost published much earlier (Monogenesis, polygenesis, and militant agnosticism I have already pointed to this problem, as it is quite different from biology, where independent evolution of life is usually not assumed by scholars, while linguistic research can never really exclude it.

While proving language relationship of closely related languages is often a complete no-brainer, it becomes especially then hard, when exceeding some critical time depth. Where this time depth lies is not clear by now, but based on our observations regarding the paste in which languages replace existing words with new ones, borrow words, or loose and build grammatical structures, it is clear that it is theoretically possible that a language group could have lost all hints on its ancestry after 5,000 to 10,000 years. Luckily, what is theoretically possible for one language, does not necessarily happen with all languages in a given sample, and as a result, we find still enough signal for ancestral languages in quite a few language families of the world, that allows us to draw conclusions that go back about 10,000 years in the most cases, if not even deeper in some cases.

Traditional insights into the proof of language relationships

The difficulty of the task is probably obvious without further explanation — the more material a language acquires from its neighbors, and the more it loses or modifies the material it inherited from its ancestors, the more difficult it is for the experts to find the evidence that convinces their colleagues about the phylogenetic affiliation of such a language. While regular sound changes can easily convince people of phylogenetic relationship, the evidence that scholars propose for deeper linguistic groupings is rarely large enough to establish correspondences.

As a result, scholars often resort to other types of evidence, such as certain grammatical peculiarities, certain similarities in the pronunciation of certain words, or external findings (e.g.,from archaeology). As Handel (2008) points out, for example, a good indicator of a Sino-Tibetan language is that its words for five, I, and fish start with similar initial sounds and contain a similar vowel (compare Chinese wǔ, wǒ, and yú, going back to MC readings ŋjuX. ŋaX, and ŋjo). While these arguments are often intuitively very convincing (and may also be statistically convincing, as Nichols 1996 argues), this kind of evidence, as mentioned by Handel, is extremely difficult to detect, since the commonalities can be found in so many different regions of a human language system.

While linguists also use sound correspondences to prove and establish relationship, there are no convincing cases known to me in which sound correspondences were employed to prove relationships beyond a certain time depth. One can compare this endeavor to some degree with the work of police commissars who have to find a murderer, and can do so easily if the person responsible left DNA at the spot, while they have to spend many nights in pubs, drinking cheap beer and smoking bad cigarettes, in order to wait for the spark of inspiration that delivers the ultimate proof not based on DNA.

Computational and statistical approaches

Up to now, no computational methods are available to find signals of the kind presented by Handel for Sino-Tibetan, i.e, a general-purpose heuristic to search for what Nichols (1996) calls individual-identifying evidence. So,computational and statistical methods have so far been based on very schematic approaches, which are almost exclusively based on wordlists. A wordlist can hereby be thought of as a simple table with a certain number of concepts (arm, hand, stone, cinema) in the first column, and translation equivalents for these concepts being listed for several different languages in the following columns (see List 2014: 22-24). This format can of course be enhanced (Forkel et al. 2018), but it represents the standard way in which many historical linguists still prepare and curate their data.

What scholars now try to do is to see if they can find some kind of signal in the data that they think would be unlikely to be detected by chance. In general, there are two ways that scholars have explored so far. In the approach proposed by Ringe (1992), the signalsthat are tested for in the wordlists are sound correspondences, and we can therefore call theses approaches correspondence-based approaches to prove language relationship. In the approach of Baxter and Manaster Ramer (2000), which follows the original idea of Dolgopolsky, the data are converted to sound classes first, and cognacy is assumed for words with identical sound classes. Sound-class-based approaches again try to illustrate that the matches that can be identified are unlikely to be due to chance.

Both approaches have been discussed in quite a range of different papers, and scholars have also tried to propose improvements to the methods. Ringe's correspondence-based approach showed that it can become difficult to prove the relationship of languages formally, although we have very good reasons to assume it based on our standard methods. Baxter and Manaster Ramer (2000) presented a more optimistic case study, in which they argue that their sound-class-based approach would allow them to argue in favor of the relationship of Hindi and English, even if the two languages are separated by at least 10,000 or even more years.

A general problem of Ringe's approach was that he tried to use combinatorics to arrive at his statistical evaluation. This is similar to the way in which Henikoff and Henikoff (1992) developed their BLOSUM matrices for biology, by assuming that the only factor that handles the combination of amino acids in biological sequences is their frequency. Ringe tried to estimate the likelihood of finding matches of word-initial consonants in his data by using a combinatorial approach based on the assumption of simple sound frequencies in the word lists he investigated. The general problem with linguistic sequences, however, is that they are not randomly arranged. Instead, every language has its own system of phonotactic rules, a rather simple grammar that restricts certain letter combinations and favors others. All spoken languages have these systems, and some vary greatly with respect to their phonotactics. As a result, due to the inherent structure of sequences, a bag of symbols approach, as used by Ringe, can have unwanted side effects and invoke misleading estimates regarding the probability of certain matches.

To avoid this problem, Kessler (2001) proposed the use of permutation tests, by which the random distribution, against which the attested distribution is compared, is generated via the shuffling of the lists. Instead of comparing translations for "apple" in one language with translations for "apple" in another language, one compares now translations for pear with translations for "apple", hoping that this — if done often enough — better approximates the random distribution (i.e. the situation in which one compares several known unrelated languages with similar phoneme inventories).

Permutation is also the standard in all sound-correspondence-based approaches. In a recent paper, Kassian et al. (2015) used these approaches (first proposed by Turchin et al. 2010) to argue for the relationship of Indo-European and Uralic languages by comparing reconstructed word lists for Proto-Indo-European and Proto-Uralic. As can be seen from the discussion of these findings involving multiple authors, people are still not automatically convinced by a significance test, and scholars have criticized: their choice of test concepts (they used the classical 110-item list by Yakhontov and Starostin), their choice of reconstruction system (they did not use the mysterious laryngeals in their comparison), and the possibility that the findings were due to other factors (early borrowing).

While there have been some more attempts to improve the correspondence-based and the sound-class-based approaches (e.g., Kessler 2007, Kilani 2015, Mortarino 2009), it is unlikely that they will lead to the consolidation of contested proposals on macro families any time soon. Apart from the general problems of many of the current tests, there seem to be too many unknowns that prevent the community to accept findings, no matter "how" significant they appear. As can be nicely seen from the reaction to the paper by Kassian et al. 2015, a significant test will first raise the typical questions regarding the quality of the data and the initial judgments (which may also at times be biased). Even if all scholars would agree in this case, however, i.e. if one could not criticize anything in the initial test setting, there would still be the possibility to say that the findings reflect early language contact instead of phylogenetic relatedness.

Initial ideas for improvement

What I find unsatisfying about most existing tests is that they do not make exhaustive use of alignment methods. The sound-class-based approach is a shortcut for alignments, but it reduces words to two consonant classes only, and requires an extensive analysis of the words to compare only the root morpheme. It therefore also opens the possibility to bias the results (even if scholars may not intend that directly). While correspondence-based tests are much more elegant in general, they avoid alignments completely, and just pick the first letter in every word. The problem seems to be that — even when using permutations to generate the random distribution — nobody really knows how one should score the significance of sound correspondences in aligned words. I have to admit that I do not know it either. Although the tools for automated sequence comparison that my colleagues and I have been developing in the past (List 2014, List et al. 2018) seem like the best starting point to improve the correspondence-based approach, it is not clear how the test should be performed in the end.

Additionally, I assume also that expanded, fully fledged, tests will ultimately show what I reported back in my dissertation — if we work on limited wordlists, with only 200 items per language, the test will drastically lose its power when certain time depths have been reached. While we can easily prove the relationship of English and German, even with only 100 words, we have a hard time doing the same thing for English and Albanian (see List 2014: 200-203). But expanding the wordlists bears another risk for comparison (as pointed out to me by George Starostin): the more words we add, the more likely it is that they have been borrowed. Thus, we face a general dilemma in historical linguistics: that we are forced to deal with sparse data, since languages tend to lose their historical signal rather quickly.

Outlook

While there is no doubt that it would be attractive to have a test that would immediately tell one whether languages are related or not, I am becoming more and more skeptical about whether this test would actually help us, specifically when concentrating on pairwise tests alone. The challenge of this problem is not just to design a test that makes sense and does not overly simplify. The challenge is to propagate the test in such a way that it convinces our colleagues that it really works. This, however, is a challenge that is greater than any of the other open problems I have discussed so far in this year.

References

Baxter, William H. and Manaster Ramer, Alexis (2000) Beyond lumping and splitting: Probabilistic issues in historical linguistics. In: Renfrew, Colin and McMahon, April and Trask, Larry (eds.) Time Depth in Historical Linguistics. Cambridge:McDonald Institute for Archaeological Research, pp. 167-188.

Campbell, Lyle and Poser, William John (2008) Language Classification: History and Method. Cambridge:Cambridge University Press.

Dolgopolsky, Aron B. (1964) Gipoteza drevnejšego rodstva jazykovych semej Severnoj Evrazii s verojatnostej točky zrenija [A probabilistic hypothesis concering the oldest relationships among the language families of Northern Eurasia]. Voprosy Jazykoznanija 2: 53-63.

Forkel, Robert and List, Johann-Mattis and Greenhill, Simon J. and Rzymski, Christoph and Bank, Sebastian and Cysouw, Michael and Hammarström, Harald and Haspelmath, Martin and Kaiping, Gereon A. and Gray, Russell D. (2018) Cross-linguistic data formats, advancing data sharing and re-use in comparative linguistics. Scientific Data 5: 1-10.

Handel, Zev (2008) What is Sino-Tibetan? Snapshot of a field and a language family in flux. Language and Linguistics Compass 2: 422-441.

Henikoff, Steven and Henikoff, Jorja G. (1992) Amino acid substitution matrices from protein blocks. Proceedings of the National Academy of Sciences 89: 10915-10919.

Jones, William (1798) The third anniversary discourse, delivered 2 February, 1786, by the president. On the Hindus. Asiatick Researches 1: 415-43.

Kassian, Alexei and Zhivlov, Mikhail and Starostin, George S. (2015) Proto-Indo-European-Uralic comparison from the probabilistic point of view. The Journal of Indo-European Studies 43: 301-347.

Kessler, Brett (2001) The Significance of Word Lists. Statistical Tests for Investigating Historical Connections Between Languages. Stanford: CSLI Publications.

Kessler, Brett (2007) Word similarity metrics and multilateral comparison. In: Proceedings of Ninth Meeting of the ACL Special Interest Group in Computational Morphology and Phonology, pp. 6-14.

Kilani, Marwan (2015): Calculating false cognates: An extension of the Baxter & Manaster-Ramer solution and its application to the case of Pre-Greek. Diachronica 32: 331-364.

List, Johann-Mattis (2014) Sequence Comparison in Historical Linguistics. Düsseldorf: Düsseldorf University Press.

List, Johann-Mattis and Walworth, Mary and Greenhill, Simon J. and Tresoldi, Tiago and Forkel, Robert (2018) Sequence comparison in computational historical linguistics. Journal of Language Evolution 3: 130–144.

Mortarino, Cinzia (2009) An improved statistical test for historical linguistics. Statistical Methods and Applications 18: 193-204.

Nichols, Johanna (1996) The comparative method as heuristic. In: Durie, Mark (ed.) The Comparative Method Reviewed. New York:Oxford University Press, pp. 39-71.

Ringe, Donald A. (1992) On calculating the factor of chance in language comparison. Transactions of the American Philosophical Society 82: 1-110.

Ross, Malcolm D. (1996) Contact-induced change and the comparative method. Cases from Papua New Guinea. In: Durie, Mark (ed.) The Comparative Method Reviewed. New York: Oxford University Press, pp. 180-217.

Turchin, Peter and Peiros, Ilja and Gell-Mann, Murray (2010) Analyzing genetic connections between languages by matching consonant classes. Journal of Language Relationship 3: 117-126.

Comparing reconstruction systems in historical linguistics

The term linguistic reconstruction has a very specific meaning in historical linguistics, pointing usually to the techniques that are used in order to infer how a given language was originally pronounced, even though it has not been attested in written sources. In previous posts, I have occasionally pointed to reconstructed forms, the so-called proto-forms, which linguists usually mark as such by putting an asterisk in from of them. For example, the word Indo-European *ph₂tér- is a reconstructed proto-form for the supposed Indo-European word "father".

While the reconstruction techniques are usually limited to languages for which we have no written record, they can in principle also be applied in order to find out how ancient languages like, for example, Latin and Greek, were pronounced in detail (Sturtevant 1920). For languages like Chinese, whose writing system leaves almost no clues about pronunciation, linguistic reconstruction is the only way to investigate the pronunciation of the oldest stages of the language.

When dealing with different reconstruction systems for Old Chinese phonology, it is quite difficult, even for experienced scholars, to spot the actual differences between the systems. That these differences exist, and that they can be quite substantial, is beyond question — and easy to understand, if one takes into account that Old Chinese is reconstructed with the help of a philological (as opposed to a mainly comparative) approach, by which data from different sources is sifted and individually weighed (cf. Jarceva 1990: 409 and List 2008).

When comparing different reconstruction systems, it is not enough simply to look at the inventories of proto-phonemes proposed by different scholars. Even if two proto-inventories (the sets of the reconstructed sounds) are exactly the same, it is possible that scholars will provide different reconstructions for individual characters. The only way to compare two or more reconstruction systems is therefore to compare the concrete reconstructions for a certain number of characters.

In addition to the sample of words, however, we also need a clear account of which segments (which proto-sounds) should be compared with each other. When comparing proto-forms for Chinese yī 一 ‘one’ in different Old Chinese reconstruction systems, such as Karlgren (1950) *ʔi ̯ĕt, Li (1971) *ʔjit, Wáng (1980) *iet, and Baxter and Sagart (2014) *ʔi[t], we would obviously not compare the medial *i ̯ of Karlgren with the initial *ʔ of Baxter and Sagart.

When adding more reconstructions, such as the one for qī 七 ‘seven’ across the four systems, for which the authors give *ts'i ̯ĕt, *tshjit, *tshiet, and *[tsh]i[t], respectively, we can further see that there are not only differences for the different segments in the same positions, but also for the interpretation of the words. Although all authors give different medials, main vowels, and finals in the two words, they are structurally consistent in giving both words the same sound segments for medial, nucleus, and coda.

What we can see from this example is that any difference in the sound segments, like the choice of initials, or the concrete solution proposed for a problem, does not immediately reflect important differences in the reconstruction systems. If two scholars just choose another symbol for a distinction that they both recognize and acknowledge, this does not render the reconstructions incompatible. It should therefore not be used as a criterion for dismissing a given reconstruction system, at least not in a first step. If two systems are structurally equivalent, then they have equivalent predictive power for the descendant language(s) they are supposed to reconstruct.

This abstractionist notion of proto-forms, which can be found in the early work of Saussure (1916) and Meillet (1903), is problematic for the endeavour of linguistic reconstruction, and usually not strictly followed (Lass 2017). Nevertheless, the potentially abstract notion of proto-forms is important to be kept in mind when comparing different reconstruction systems. When distinguishing the structural differences (which result from the direct interpretation of the data and the identification of regular sound correspondences) from the substantial differences (resulting from a phonetic and phonological interpretation of the identified correspondences), we have a much clearer account of the core of the differences, and whether they are worth our consideration or not.

But how can we compare reconstruction systems structurally? Firstly, we need to have the data assembled in aligned form, in order to make sure that we only compare like with like (e.g., medial with medial, and vowel with vowel). A sample illustration in which alignments of the proto-forms for ‘seven’ and ‘one’, produced with the help of the EDICTOR tool (List 2017), is given in the figure below.

Comparing reconstruction proposals with the help of alignments.

Alternatively, we can also select a single aspect, such as, for example, the vowel system proposed in different reconstruction systems. Having assembled a substantial amount of different proto-forms in this way, the structural comparison between different reconstruction systems can be modeled as a comparison of different cluster analyses, or, more accurately, partitioning analyses. A partitioning analysis assigns a given number of objects to a certain number of different groups. When dealing only with the vowels proposed by different reconstruction systems, we can say that a given reconstruction, like the one by Karlgren, for example, assigns each Chinese character, for which a proto-form is given, to a particular group depending on the main vowel selected for the reconstruction.

If, for a given number of reconstructions, we model each reconstruction system as a partitioning analysis, based on the main vowel proposed by the system, we can use standard metrics from graph theory and Natural Language Processing to compare different reconstruction systems with each other. Very straight-forward measures for the comparison of two partitioning analyses are the so-called B-Cubed scores (Amigó et al. 2009), which have proven specifically useful for the evaluation of automatic cognate detection methods in historical linguistics, compared to a gold standard (Hauer and Kondrak 2011, List et al. 2017).

Being an evaluation measure, B-Cubed scores come in the typical three flavors of precision, recall, and F-Score. Precision is similar to the notion of true positives, and recall is similar to true negatives. For the purpose of comparing reconstruction systems, only the F-score is needed, as it is a symmetric measure, and the notion of true positives and true negatives is meaningless, unless we decide that we blindly trust one of the given systems. As also for the scores for precision and recall, the F-score ranges between 0 and 1, with 1 indicating that the two partitioning analyses are identical.

In order to compare more than one reconstruction system, we can make use of techniques for exploratory data analysis (Morrison 2014); and the most straightforward way to do this, is, of course, to use the NeighborNet algorithm (Bryant and Moulton 2004), as provided by the SplitsTree package (Huson 1998).

In order to illustrate how data-display networks can be used to study differences among Old Chinese reconstruction systems, I designed a little experiment, based on data taken from (List et al. 2017b), who provide Old Chinese reconstructions for all rhyme words in the Shījīng based on eight different reconstruction systems (Baxter and Sagart 2014, Karlgren 1950, Li 1971, Pān 2000, Schuessler 2007, Starostin 1989, Wáng 1980, Zhèngzhāng 2003).

In order to keep the analysis simple, I extracted only the different reconstructions of the main vowel for each character in each system, and carried out a pairwise comparison of all eight systems, computing the B-Cubed F-scores for each pair, omitting characters for which no reconstruction could be found in the data. These scores were then converted to a distance matrix, and fed to the NeighborNet algorithm (the source code can be downloaded here). The resulting network is provided in the figure below.

NeighorNet reflecting the closeness of the different reconstruction systems

As one can see, the data roughly clusters into three subgroups, namely Schuessler, Baxter and Sagart, and Starostin vs. Pān and Zhèngzhāng vs. Karlgren, Li, and Wáng. On a larger scale, we can divide the data into all six-vowel systems versus the non-six-vowel systems (Karlgren, Wáng, Li). Given that Pān is a direct student of Zhèngzhāng, the closeness between their reconstruction systems is not surprising.

What may be surprising is the closeness of the Schuessler, Starostin, and Baxter and Sagart systems, given their notable differences with respect to the criterion of vowel purity tested by List et al. (2017b). Even if the network analysis cannot directly explain all of these differences in detail, it seems like a worthwhile enterprise, which should be further expanded by comparing not only the vowels, but fully aligned proto-forms.

Given the straightforwardness of the application, it seems also useful to test it on other language families where there is similar disagreement, as in the reconstruction of Old Chinese phonology.

References

Amigó, E., J. Gonzalo, J. Artiles, and F. Verdejo (2009): A comparison of extrinsic clustering evaluation metrics based on formal constraints. Information Retrieval 12.4. 461-486.

Baxter, W. and L. Sagart (2014) Old Chinese: a new reconstruction. Oxford University Press: Oxford.

Bryant, D. and V. Moulton (2004) Neighbor-Net. An agglomerative method for the construction of phylogenetic networks. Molecular Biology and Evolution 21.2. 255-265.

Hauer, B. and G. Kondrak (2011) Clustering semantically equivalent words into cognate sets in multilingual lists. In: Proceedings of the 5th International Joint Conference on Natural Language Processing. AFNLP 865-873.

Huson, D. (1998) SplitsTree: analyzing and visualizing evolutionary data. Bioinformatics 14.1. 68-73.

Jarceva, V. (1990) Sovetskaja Enciklopedija: Moscow.

Karlgren, B. (1950) The Book of Odes. Chinese text, transcription and translation. Museum of Far Eastern Antiquities: Stockholm.

Lass, R. (2017) Reality in a soft science: the metaphonology of historical reconstruction. Papers in Historical Phonology 2.1. 152-163.

Li Fang-kuei 李方桂 (1971) Shànggǔyīn yánjiū 上古音研究 [Studies on Archaic Chinese phonology]. Qīnghuá Xuébào 清華學報 9.1-2. 1-60.

List, J.-M. (2008) Rekonstruktion der Aussprache des Mittel- und Altchinesischen. Vergleich der Rekonstruktionsmethoden der indogermanischen und der chinesischen Sprachwissenschaft [Reconstruction of the pronunciation of Middle and Old Chinese. Comparison of reconstruction methods in Indo-European and Chinese linguistics]. Magister thesis. Freie Universität Berlin: Berlin.

List, J.-M., S. Greenhill, and R. Gray (2017) The potential of automatic word comparison for historical linguistics. PLOS One 12.1. 1-18.

List, J.-M. (2017) A web-based interactive tool for creating, inspecting, editing, and publishing etymological datasets. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. System Demonstrations. 9-12.

List, J.-M., J. Pathmanathan, N. Hill, E. Bapteste, and P. Lopez (2017) Vowel purity and rhyme evidence in Old Chinese reconstruction. Lingua Sinica 3.1. 1-17.

Meillet, A. (1903) Introduction à l’étude comparative des langues indo-européennes. Hachette: Paris.

Morrison, D.A. (2014) Phylogenetic networks: a new form of multivariate data summary for data mining and exploratory data analysis. WIREs Data Mining and Knowledge Discovery 4: 296-312.

Pān Wùyún 潘悟云 (2000) Hànyǔ lìshǐ yīnyùnxué 汉语历史音韵学 [Chinese historical phonology]. Shànghǎi Jiàoyù 上海教育: Shànghǎi 上海.

de Saussure, F. (1916) Cours de linguistique générale. Payot: Lausanne.

Schuessler, A. (2007) ABC Etymological dictionary of Old Chinese. University of Hawai’i Press: Honolulu.

Starostin, S. (1989) Sravnitel’no-istoričeskoe jazykoznanie i leksikostatistika [Comparative-historical linguistics and lexicostatistics]. In: Kullanda, S., J. Longinov, A. Militarev, E. Nosenko, and V. Shnirel’man (eds.): Lingvističeskaja rekonstrukcija i drevnejšaja istorija VostokaMaterialy k diskussijam na konferencii.[Materials for the discussion on the conference].1. Institut Vostokovedenija: Moscow. 3-39.

Sturtevant, E. (1920) The pronunciation of Greek and Latin. University of Chicago Press: Chicago.

Zhèngzhāng Shàngfāng 郑张尚芳 (2003) Shànggǔ yīnxì 上古音系 [Old Chinese phonology]. Shànghǎi Jiàoyù 上海教育: Shànghǎi 上海.

Why we need alignments in historical linguistics

Alignments have been discussed quite a few times in this blog. They are so extremely common in molecular biology that I doubt that there are any debates about their usefulness, apart from certain attempts to improve the modelling, especially in cases of non-colinear patterns (Kehr et al. 2014), or to speed up computation (Mathura and Adlakha 2016). In linguistics, on the other hand, alignments are rarely used, although initial attempts to arrange homologous words in a matrix go back to the early 20th century, as you can see from this example taken from Dixon and Koerber (1919: 61):

Early alignment from Dixon and Kroeber (1919)

This example is rather difficult to read for those not familiar with the annotation. The authors group homologous words across different indigenous languages from California. The group labels of the languages under investigation are given in abbreviated form at the very left of the matrix, and the actual varieties are listed in the next column. What follows is the actual alignment, along with comments in the last column. Regarding the alignments, the authors note on page 55:

A number of sets of cognates have been taken from their numbered place in this list and put at the end to allow of their being printed in columnar form, with a view to bringing out parallelisms that otherwise might fail to impress without detailed analysis and discussion. (Dixon and Kroeber 1919: 55)

In my opinion, this expresses nicely why alignments should be used more often in linguistics — due to the problem that our "alphabets" (the sound systems of languages) are undergoing constant change (see this earlier post for details regarding this claim), we need to infer both the scoring function between different sounds across different languages, and the alignment at the same time. If we look at the similarities the authors spotted, it should become obvious what I mean.

I am not yet sure how to interpret the data exactly, but if I am not mistaken, the authors claim that each of the column contains homologous material. So, they find a similarity between kaha in the first row (the language is Northern Wintun, according to the key to abbreviations in the book), and tu in the last row (Monterey Costanoan). The last column shows suffixes, which I think the authors exclude from their analysis, but I could not find additional information confirming this in their book.

The comment column illustrates another problem of representation, namely that the authors do not know how to handle cases of metathesis (or transpositions) consistently. The transposition of the parts of words is a process that is quite frequent in language evolution. It is very frequent in compounds consisting of modifier and modified, such as milk coffee in English, where milk modifies the coffee, while French, for example, puts the modifier after the main noun, expressing this as café au lait.

Nowadays, we can handle these cases consistently in linguistics, both in our data annotation and in the alignments, and we can even search for the structures automatically (see List et al. 2016). One hundred years ago, when Dixon and Kroeber worked out their comparison of the languages in California, they were pioneers who tried to increase the transparency of our discipline, and it is clear that their solutions are not completely satisfying from today's perspective.

It is extremely surprising for me that, despite these early attempts to make our homology judgments in linguistics more transparent, the practice of phonetic alignments is still rarely used by historical linguists. Indeed, the majority of them even think that it is a waste of time, or only useful for the purpose of teaching.

I was reminded of this when I looked at a recent proposal by Bengtson (2017, see also this blog for details) for deep genetic connections between Basque and North Caucasian languages. Note that the Basque language is traditionally considered as an isolate, i.e. a language whose nearest relatives we cannot find among the languages in the world. Many linguists have attempted to solve this puzzle by proposing various hypotheses (see Forni 2013 for an example of attempting to link Basque with Indo-European). Bengtson proposes various types of evidence, which I cannot really judge, as I do not know the languages under comparison, but finally, he also shows a list with potential homologs between Basque and North Caucasian varieties, which you find below.

Potential homologs between Basque and North Caucasian languages (Bengtson 2017)

If you are not a trained historical linguistic, and thus do not know what to do with this table, be assured that many historical linguists will feel similarly. As a rough explanation: the concepts are supposed to be very, very stable, being drawn from Sergey Yakhontov's list of 35 ultra-stable concepts, and I think that all words in one row are supposed to be etymologically related — that is, they should be potential homologs across all of the languages. If word forms are preceded by the asterisk symbol (*), this means that they are reconstructed, i.e. not reflected in written sources. But that is all I can tell you for the moment. Where I should start the comparison between the words remains a mystery for me, as I do not know which parts are supposed to be similar. Alignments would help us to see immediately where the author thinks that the historical similarities can be found — that is, we would see, which parts of the words are supposed to be homologous.

At this point in the post, I originally planned to provide you with an alignment of Bengtson's table, in order to illustrate the benefits of alignment in linguistics. Unfortunately, I had to admit to myself that I cannot do this, as I simply do not know where to align the words (apart from some rare trivial cases in the table).

I really hope that this will change in the future. Too often, our hypotheses in linguistics suffer from insufficient transparency with regards to the "proofs" and the evidence. I agree that it is very difficult to come up with good alignments in linguistics, especially if one regards cases of metathesis, unrelated parts, and general uncertainty. However, instead of giving in to the problem, we should follow the pioneering work of Dixon and Kroeber, and try to improve the way we present our data to both our colleagues and a broader public.

Theories such as the link between Basque and the North Caucasian languages are usually highly disputed in historical linguistics, and I do not know of any long range proposal that has gained broad acceptance during the last 50 years. Yet, maybe this is not because the proposals are not valid, but simply because those who are proposing these theories have failed to present their findings in a transparent and testable way.

References

• Bengtson, J. (2017) The Euskaro-Caucasian Hypothesis. Current model. PDF.
• Dixon, R. and A. Kroeber (1919) Linguistic families of California. University of California Press: Berkeley.
• Forni, G. (2013) Evidence for Basque as an Indo-European language. The Journal of Indo-European Studies 41.1 & 2: 1-142.
• Kehr, B., K. Trappe, M. Holtgrewe, and K. Reinert (2014) Genome alignment with graph data structures: a comparison. BMC Bioinformatics 15.1: 99.
• List, J.-M., P. Lopez, and E. Bapteste (2016) Using sequence similarity networks to identify partial cognates in multilingual wordlists. In: Proceedings of the Association of Computational Linguistics 2016 (Volume 2: Short Papers). Association of Computational Linguistics, pp. 599-605.
• Mathur, R. and N. Adlakha (2016) A graph theoretic model for prediction of reticulation events and phylogenetic networks for DNA sequences. Egyptian Journal of Basic and Applied Sciences 3.3: 263-271.