Evolution unchained: The development of person names and the limits of sequences

What do person names like Jack and Hans have in common, and what unites Joe and Pepe? Both name pairs go back to a common ancestor. For Jack and Hans, this would be John (ultimately going back to Iōánnēs in Greek), and for Joe and Pepe, this would be Josef (originally from Hebrew). Given the striking dissimilarity of the names in their current form, the pathways of change by which they have evolved into their current shape are quite complicated.

While the German name Hans can be easily shown to be a short form of the German variant Johannes, the evolution of Jack is more complicated. First (at least this is what people on Wikipedia suppose), Iōánnēs becomes John in English, similar to the process that transformed German Johannes into Hans. Then, in an ancient form of English, a diminutive was built for John, which yielded the form Jenkin, with the diminutive suffix -kin that has a homologous counterpart in German -chen (which can be attached to Hans as well, yielding Hänschen). Etymologically, Jack is little Johnny.

While Joe in English is a shortening of Josef, the development of Pepe is again a bit more complex. First, we find the form Giuseppe as an Italian counterpart of Josef. How this form then yielded Pepe as a diminutive is not completely clear to me; but since we find the pe in the Italian form, we can think of a process by which Giuseppe becomes Giuseppepe, leaving Pepe after the deletion of the initial two syllables.

The complexity of person-name evolution

Even from these two examples alone, we can already see that the evolution of person names can easily become quite complex. If all words in all spoken languages in the world evolved in the same way in which our person names evolve, we would have a big problem in historical linguistics, since the amount of speculation in our etymologies would drastically increase.

When comparing etymologically related words from different languages, we generally assume that they show regular correspondences among their sound segments. This presupposes that there is still enough sound material that reflects these correspondences, allowing us to detect and assess them. But since the evolution of person names rarely consists of the regular modification of sounds, but rather results in the deletion, reduplication, and rearrangement of whole word parts, there is rarely enough left in the end that could be used as the basis for a classical sequence comparison.

With the name Tina in German being the short form of Bettina, Christina, and at times even Katharina, and with Bettina itself going back to Elisabeth, and with Tina becoming Tinchen, Tinka, or Tine, we face an almost insurmountable challenge when trying to model the complexity of the various patterns by which names can change.

Modeling word derivation with directed networks

That words do not evolve solely by the alternation of sounds, but also by different forms of derivation, is nothing new for historical linguistics. We face the problem, for example, when looking for etymologically related words in the basic lexicon of phylogenetically related languages. However, these phenomena can be easily investigated by enhanced means of annotation. The evolution of person names, on the other hand, presents us with larger challenges.

While working as a research fellow in France in 2015-2016, I had the time to develop a small tool that allows us to represent derivational relations between related words with help of a directed network, and thus allows us to model these relations in a rough way. Such a graph is directed, and our words are the nodes in the network, with the edges drawn between the assumed ancestor word forms and their descendants. This tool, which I then called DeriViz, is still available online. and makes it possible to visualize network relations between words.

I have now conducted a small experiment with this tool, by taking name variants of Elisabeth, as they are listed in Wikipedia, and trying to model them in a directed network, along with intermediate stages. You can do this easily yourself, by copying the network that I have constructed in text form below, and pasting it into the field for data entry at the DeriViz-Homepage. The network will be visualized when you press on the OK button; and you can play with it by dragging it around.

Elisabeth → BETT
BETT → Betty
BETT → Bettina
BETT → Bettine
BETT → Betsi
Elisabeth → ELISABETH
ELISABETH → Elise
ELISABETH → Elsbeth
ELISABETH → Else
ELISABETH → Elina
Elisabeth → ILSA
ILSA → Ilsa
ILSA → Ilse
Elisabeth → Isabella
Elisabeth → LISA
LISA → Lieschen
LISA → Liese
LISA → Liesel
LISA → Lis
LISA → Lisa
LISA → Lisbeth
LISA → Lisette
LISA → Lise
LISA → Liesl
Elisabeth → LILA 
LISA → Lila
LISA → Liliane
LISA → Lilian
LISA → Lilli
Elisabeth → Sisi

I intentionally reduced the amount of data here, in order to make sure that the graphic can still be inspected. But it is clear that even this simple model, which assumes unique ancestor-descendant relations among all of the derived person names, is stretched to its limits when applied to names as productive as Elisabeth, at least as far as the visualization is concerned.

Derivation network of names derived from Elisabeth

If you now imagine that there are various processes that turn an ancestral name into a descendant name, and that one would ideally want to model the differences between these processes as well, one can see easily that it is indeed not a trivial problem to model the evolution of person names (and we are not even speaking of inferring any of these relations).

How names evolve

Names evolve in various ways along different dimensions. With respect to their primary function, or their use, we tend to use, among others, nick names. Formally, nick names are often a short form of an original name, but depending on the community of speakers, it is also possible that there is a formal procedure by which a nick name can be derived from a base name. Thus, every speaker of Russian should know that Jekaterina can be turned into Katerina, which can be turned into Katja, which can be turned into Katjuscha, or, in the case of a Vocative, into Katj. Once the primary function of a name changes, its form usually also changes, as we can now see in many examples.

But the form can also change when a name crosses language borders. If you go with your name into another country, and the speakers have problems pronouncing certain sounds that occur in your name, it is very likely that they will adjust your name's pronunciation to the phonetic needs of their own language, and modify it. Names cross language borders very quickly, since we tend not to leave them at home when visiting or migrating to foreign countries. As a result, a great deal of the diversity of person names  observed today is due to the migration of names across the world's larger linguistic communities.

How we change names when building short forms or nick names, or when trying to adapt a name to a given target language, depends on the structure of the language. The most important part is the phonology of the language in which the change happens. For example, when transferring a name from one language to another, and the new language lacks some of the sounds in the original name, speakers will replace them with those sounds which they perceive to be closest to the lacking ones.

But the modification is not restricted to the replacement of sounds. My own given name, Mattis, for example, usually has the stress on the first syllable, but in France, most people tend to call me Matisse, with the accent on the second syllable, reflecting the general tendency to stress the last syllable of a word in French. In Russian, on the other hand, Mattis could be perfectly pronounced, but since people do not know the name, they often confuse it with its variant Matthias, which then sounds like Matjes when pronounced in Russian (which is the name for soused herring in Germany). There are more extreme cases; and both English and German speakers are also good at drastically adjusting foreign names to the needs of their mother tongues.

It would be nice if it was possible to investigate the huge diversity in the evolution of person names more systematically. In principle, this should be possible. I think, starting from directed networks is definitely a good idea; but it would probably have to be extended by distinguishing different types of graph edges. Even if a given selection may not handle all of the processes known to us, it might help to collect some primary data in the first place.

With a large enough set of well-annotated data, on the other hand, one might start to look into the development of algorithms that could infer derivation relationships between person names; or one could analyze the data and search for the most frequent processes of person name evolution. Many more analyses might be possible. One could see to which degree the processes differ across languages, or how names migrate from one language to another across times, usage types, and maybe even across fashions.

Outlook

I assume that the result of such a collection would be interesting not only for couples who are about to replicate themselves, but would also be interesting for historical research and research in the field of cultural evolution. Whether such a collection will ever exist, however, seems less likely. The problem is that there are not enough scholars in the world who would be interested in this topic, as one can see from the very small number of studies that have been devoted to the problem up to now (as one of the few exceptions known to me, compare the nice overview of person name classification by Handschuh 2019). I myself would not be able to help in this endeavour, given that I lack the scholarly competence of investigating name evolution. But I would sure like to investigate and inspect the results, if they every become available.

Reference

Handschuh, Corinna (2019) The classification of names. A crosslinguistic study of sex-specific forms, classifiers, and gender marking on personal names. STUF — Language Typology and Universals 72.4: 539-572.

How should one study language evolution?

This is a joint post by Justin Power, Guido Grimm, and Johann-Mattis List.

Like in biology, we have two basic possibilities for studying how languages evolve:

• We set up a list of universal comparanda. These should occur in all languages and show a high enough degree of variation that we can use them as indicators of how languages have evolved;
• We create individual lists of comparanda. These are specific for certain language groups that we want to study.

Universal comparanda

While most studies would probably aim to employ a set of universal comparanda, the practice often requires a compromise solution in which some non-universal characteristics are added. This holds, for example, for the idea of a core genome in biology, which ends up being so small in overlap across all living species that it makes little sense to compute phylogenies based on it, except for for closely related species (Dagan and Martin 2006). Another example is the all-inclusive matrices that are used to establish evolutionary relationships of extinct animals characterized by high levels of missing data (eg. Tschopp et al. 2015; Hartman et al. 2019). The same holds for historical linguistics, with the idea of a basic lexicon or basic vocabulary, represented by a list of basic concepts that are supposed to be expressed by simple words in every human language (Swadesh 1955), given that the number of concepts represented by simple words shared across all human languages is extremely small (Hoijer 1956).

Figure 1: All humans have hands and arms but some words for ‘hands’ and ‘arms’ address different things (see our previous post "How languages loose body parts").

Apart from the problem that basic vocabulary concepts occurring in all languages may be extremely limited, test items need to fulfill additional characteristics that may not be easy to find,in order to be useful for phylogenetic studies. They should, for example, be rather resistant to processes of lateral transfer or borrowing in linguistics. They should preferably be subject to neutral evolution, since selective pressure may lead to parallel but phylogenetically independent processes (in biology known as convergent evolution) that are difficult to distinguish and can increase the amount of noise in the data (homoplasy).

Selective pressure, as we might find, for example, in a specific association between certain concepts and certain sounds across a large phygenetically independent sample of human languages, is rarely considered to be a big problem in historical linguistics studies dealing with the evolution of spoken languages (see Blasi et al. 2016 for an exception). In sign language evolution, however, the problem may be more acute because of a similar iconic motivation of many lexical signs in phylogenetically independent sign languages (Guerra Currie et al. 2002), as well as the representation of concepts such as body parts and pronouns using indexical signs with similar forms. This latter characteristic of all known sign languages has led to the design of a basic vocabulary list that differs from those traditionally used in the historical linguistics of spoken languages (Woodward 1993); and we know of only one proposal attempting to address the problem of iconicity in sign languages for phylogenetic research (Parkhurst and Parkhurst 2003).

Figure 2: Basic processes in the evolution of languages, spoken or signed  (see our previous post How languages loose body parts).

All in all, it seems that there may be no complete solution for a list of lexical comparanda for all human languages, including sign languages, given the complexities of lexical semantics, the high variability in expression among the languages of the world (see Hymes 1960 for a detailed discussion on this problem), and the problems related to selective pressures highlighted above. Scholars have proposed alternative features for comparing languages, such as grammatical properties (Longobardi et al. 2015) or other "structural" features (Szeto et al. 2018), but these are either even more problematic for historical language comparison—given that it is never clear if these alternative features have evolved independently or due to common inheritance—or they are again based on a targeted selection for a certain group of languages in a certain region.

Targeted comparanda

If there is no universal list of features that can be used to study how languages have evolved, we have to resort to the second possibility mentioned above, by creating targeted lists of comparanda for the specific language groups whose evolution we want to study. When doing so, it is best to aim at a high degree of universality in the list of comparanda, even if one knows that complete universality cannot be achieved. This practice helps to compare a given study with alternative studies; it may also help colleagues to recycle the data, at least in part, or to merge datasets for combined analyses, if similar comparanda have been published for other languages.

But there are cases where this is not possible, especially when conducting studies where no previous data have been published, and rigorous methods for historical language comparison have yet to be established. Sign languages can, again, be seen as a good example for this case. So far, few phylogenetic studies have addressed sign language evolution, and none have supplied the data used in putting forward an evolutionary hypothesis. Furthermore, because the field lacks unified techniques for the transcription of signs, it is extremely difficult to collect lexical data for a large number of sign languages from comparable glossaries, wordlists, and dictionaries, the three primary sources, apart from fieldwork, that spoken language linguists would use in order to start a new data collection. We are aware of one comparative database with basic vocabulary for sign languages that is currently being built (Yu et al. 2018), and that may represent lexical items in a way that can be compared efficiently, but these data have not yet been made available to other researchers.

Sign languages

When Justin Power approached Mattis about three years ago, asking if he wanted to collaborate on a study relating to sign language evolution, we quickly realized that it would be infeasible to gather enough lexical data for a first study. Tiago Tresoldi, a post-doc in our group, suggested the idea of starting with sign language manual alphabets instead. From the start, it was clear that these manual alphabets might have certain disadvantages — because they are used to represent written letters of a different language, they may constitute a set of features evolving independently from the sign language itself.

Figure 3: Processes shaping manual alphabets. The evolution of signed concepts may be affected by the same, leading to congruent patterns, or different processes, leading to incongruent differentiation patterns (see our previous post: Stacking networks based on sign language manual alphabets).

But on the other hand, the data had many advantages. First, a sufficient number of examples for various European sign languages were available in online databases that could be transcribed in a uniform way. Second, the comparison itself was facilitated, since in most cases there was no ambiguity about which “concepts” to compare, in contrast to what one would encounter in a comparison of lexical entries. For example, an “a” is an “a” in all languages. Third, it turned out that for quite a few languages, historical manual alphabets could be added to the sample. This point was very important for our study. Given that scholars still have limited knowledge regarding the details of sign change in sign language evolution, it is of great importance to compare sources of the same variety, or those assumed to be the same, across time—just as spoken language linguists compared Latin with Spanish and Italian in order to study how sounds change over time. And finally, manual alphabets in fact constitute an integrated part of many sign languages that may, for example, contribute to the forms of lexical signs, making the idea more plausible that an understanding of the evolution of manual alphabets could be informative about the evolution of sign languages as a whole.

Figure 4: Early evolution of handshapes used to sign ‘g’ (see our previous post: Character cliques and networks – mapping haplotypes of manual alphabets).

Guido later joined our team, providing the expertise to analyze the data with network methods that do not assume tree-like evolution a priori. We therefore thought that we had done a rather good job when our pilot study on the evolution of sign language manual alphabets, titled Evolutionary Dynamics in the Dispersal of Sign Languages, finally appeared last month (Power et al. 2020). We identified six basic lineages from which the manual alphabets of the 40 contemporary sign languages developed. The term "lineage" was deliberately chosen in this context, since it was unclear whether the evolution of the manual alphabets should be seen as representative of the evolution of the sign languages as a whole. We also avoided the term "family", because we were wary of making potentially unwarranted assumptions about sign language evolution based on theories in historical linguistics.

Figure 5: The all-inclusive Neighbor-net (taken from Power et al. 2020).

While the study was positively received by the popular media, and even made it onto the title page of the Süddeutsche Zeitung (one of the largest daily newspapers in Germany), there were also misrepresentations of our results in some media channels. The Daily Mail (in the UK), in particular, invented the claim that all human sign languages have evolved from five European lineages. Of course, our study never said this, nor could it have, since only European sign languages were included in our sample. (We included three manual alphabets representing Arabic-based scripts from Afghan, Jordanian, and Pakistan Sign Languages, where there was some indication that these may have been informed by European sources.)

Study of phylogenetics

While we share our colleagues’ distaste for the Daily Mail’s likely purposeful misrepresentation (in the end, unfortunately, it may have achieved its purpose as click bait), some colleagues went a bit further. One critique that came up in reaction to the Daily Mail piece was that our title opens the door to misinterpretation, because we had only investigated manual alphabets and, hence, cannot say anything about the "evolutionary dynamics of sign languages".

While the title does not mention manual alphabets, it should be clear that any study on evolution is based on a certain amount of reduction. Where and how this reduction takes place is usually explained in the studies. Many debates in historical linguistics of spoken languages have centered around the question of what data are representative enough to study what scholars perceive as the "overall evolution" of languages; and scholars are far from having reached a communis opinio in this regard. At this point, we simply cannot answer the question of whether manual alphabets provide clues about sign language evolution that contrast with the languages’ "general" evolution, as expressed, for example, in selecting and comparing 100 or 200 words of basic vocabulary. We suspect that this may, indeed, be the case for some sign languages, but we simply lack the comparative data to make any claims in this respect.

Figure 6: Evolution doesn’t mean every feature has to follow the same path: a synopsis of molecular phylogenies inferred for oaks, Quercus, and their relatives, Fagaceae (upcoming post on Res.I.P.) While nuclear differentiation matches phenotypic evolution and the fossil record (likely monophyla in bold font), the evolution of the plastome is partly decoupled (gray shaded: paraphyletic clades). Likewise, we can expect that different parts of languages, such as manual alphabets vs. core “lingome” of sign languages, may indicate different relationships.

The philosophical question, however, goes much deeper, to the "nature" of language: What constitutes a language? What do all languages have in common? How do languages change? What are the best ways to study how languages evolve?

One approach to answering these questions is to compare collectible features of languages ("traits" in biology)­, and to study how they evolve. As the field develops, we may find that the evolution of a manual alphabet does not completely coincide with the evolution of the lexicon or grammar of a sign language. But would it follow from such a result that we have learned nothing about the evolution of sign languages?

There is a helpful analogy in biology: we know that different parts of the genetic code can follow different evolutionary trajectories; we also know that phenotype-based phylogenetic trees sometimes conflict with those based on genotypes. But this understanding does not stop biologists from putting forward evolutionary hypotheses for extinct organisms, where only one set of data is available (phenotypes; Tree of Life). Furthermore, such conflicting results may lead to a more comprehensive understanding of how a species has evolved.

Figure 7: A likely case of convergence: the sign for “г” in Russian and Greek Sign Language, visually depicting the letter (see our previous post Untangling vertical and horizontal processes in the evolution of handshapes). Complementing studies of signed concepts may reveal less obvious cases of convergence (or borrowing).

Because we felt the need to further clarify the intentions of our study, and to answer some of the criticism raised about the study on Twitter, we decided to prepare a short series of blog posts devoted to the general question of "How should one study language evolution" (or more generally: "How should one study evolution?"). We hope to take some of the heat out of the discussion that evolved on Twitter, by inviting those who raised critiques about our study to answer our posts in the form of comments here, or in their own blog posts.

The current blog post can thus be understood as an opening for more thoughts and, hopefully, more fruitful discussions around the question of how language evolution should be studied.

In that context, feel free to post any questions and critiques you may have about our study below, and we will aim to pick those up in future posts.

References

Damián E. Blasi and Wichmann, Søren and Hammarström, Harald and Stadler, Peter and Christiansen, Morten H. (2016) Sound–meaning association biases evidenced across thousands of languages. Proceedings of the National Academy of Science of the United States of America 113.39: 10818-10823.

Dagan, Tal and Martin, William (2006) The tree of one percent. Genome Biology 7.118: 1-7.

Guerra Currie, Anne-Marie P. and Meier, Richard P. and Walters, Keith (2002) A cross-linguistic examination of the lexicons of four signed languages. In R. P. Meier, K. Cormier, & D. Quinto-Pozos (Eds.), Modality and Structure in Signed and Spoken Languages (pp.224-236). Cambridge University Press.

Hoijer, Harry (1956) Lexicostatistics: a critique. Language 32.1: 49-60.

Hymes, D. H. (1960) Lexicostatistics so far. Current Anthropology 1.1: 3-44.

Longobardi, Giuseppe and Ghirotto, Silva and Guardiano, Cristina and Tassi, Francesca and Benazzo, Andrea and Ceolin, Andrea and Barbujan, Guido (2015) Across language families: Genome diversity mirrors linguistic variation within Europe. American Journal of Physical Anthropology 157.4: 630-640.

Parkhurst, Stephen and Parkhurst, Dianne (2003) Lexical comparisons of signed languages and the effects of iconicity. Working Papers of the Summer Institute of Linguistics, University of North Dakota Session, vol. 47.

Power, Justin M. and Grimm, Guido and List, Johann-Mattis (2020) Evolutionary dynamics in the dispersal of sign languages. Royal Society Open Science 7.1: 1-30. DOI: 10.1098/rsos.191100

Swadesh, Morris (1955) Towards greater accuracy in lexicostatistic dating. International Journal of American Linguistics 21.2: 121-137.

Szeto, Pui Yiu and Ansaldo, Umberto and Matthews, Steven (2018) Typological variation across Mandarin dialects: An areal perspective with a quantitative approach. Linguistic Typology 22.2: 233-275.

Woodward, James (1993) Lexical evidence for the existence of South Asian and East Asian sign language families. Journal of Asian Pacific Communication 4.2: 91-107.

Simulation of lexical change (Open problems in computational diversity linguistics 5)

The fifth problem in my list of open problems in computational diversity linguistics is devoted to the problem of simulating lexical change. In a broad sense, lexical change refers to the way in which the lexicon of a human language evolves over time. In a narrower sense, we would reduce it to the major processes that constitute the changes that affect the words of human languages.

Following Gevaudán (2007: 15-17), we can distinguish three different dimensions along which words can change, namely:

• the semantic dimension — a given word can change its meaning
• the morphological dimension —new words are formed from old words by combining existing words or deriving new words with help of affixes, and
• the stratic dimension — languages may acquire words from their neighbors and thus contain strata of contact.

If we take these three dimension as the basis of any linguistically meaningful system that simulates lexical change (and I would strongly argue that we should), the task of simulating lexical change can thus be worded as follows:

Create a model of lexical change that simulates how the lexicon of a given language changes over time. This model may be simplifying, but it should account for change along the major dimensions of lexical change, including morphological change, semantic change, and lexical borrowing.

Note that the focus on three dimensions along which a word can change deliberately excludes sound change (which I will treat as a separate problem in an upcoming blogpost). Excluding sound change is justified by the fact that, in the majority of cases, the process proceeds independently from semantic change, morphological change, and borrowing, while the latter three process often interact.

There are, of course, cases where sound change may trigger the other three processes — for example, in cases where sound change leads to homophonous words in a language that express contrary meanings, which is usually resolved by using another word form for one of the concepts. An example for this process can be found in Chinese, where shǒu (in modern pronunciation) came to mean both "head" and "hand" (spelled as 首 and 手). Nowadays, shǒu remains only in expressions like shǒudū 首都 "capital", while tóu 头 is the regular word for "head".

Since the number of these processes where we have sufficient evidence to infer that sound change triggered other changes is rather small, we will do better to ignore it when trying to design initial models of lexical change. Later models could, of course, combine sound change with lexical change in an overarching framework, but given how the modeling of lexical change is already complex just with the three dimensions alone, it seems useful to put it aside for the moment and treat it as a separate problem.

Why simulating lexical change is hard

For historical linguists, it is obvious why it is hard to simulate lexical change in a computational model. The reason is that all three major processes of lexical change, semantic change, morphological change, and lexical borrowing, are already hard to model and understand themselves.

Morphological change is not only difficult to understand as a process, it is even difficult to infer; and it is for this reason, that we find morphological segmentation as the first example in my list of open problems. The same holds for lexical borrowing, which I discussed as the second example in my list of open problems. The problem of common pathways of semantic change will be discussed in a later post, devoted to the general typology of semantic change processes.

If each of the individual processes that constitute lexical change is itself either hard to model or to infer, it is no wonder that the simulation is also hard.

Traditional insights into the process of lexical change

Important work on lexical change goes back at least to the 1950s, when Morris Swadesh (1909-1967) proposed his theory of lexicostatistics and glottochronology (Swadesh 1952, 1955, Lees 1953). What was important in this context was not the idea that one could compute the divergence time of languages, but the data model which Swadesh introduced. This data model is represented by a word-list in which a particular list of concepts is translated into a particular range of languages. While former work on semantic change had been mostly onomasiological — ie. form-based, taking the word as the basic unit and asking how it would change its meaning over time — the new model used concepts as a comparandum, investigating how word forms replaced each other in expressing specific contexts over time. This onomasiological or concept-based perspective has the great advantage of drastically facilitating the sampling of language data from different languages.

When comparing only specific word forms for cognacy, it is difficult to learn something about the dynamics of lexical change through time, since it is never clear how to sample those words that one wants to investigate more closely in a given study. With Swadesh's data model, the sampling process is reduced to the selection of concepts, regardless of whether one knows how many concepts one can find in a given sample of languages. Swadesh was by no means the first to propose this perspective, but he was the one who promulgated it.

Swadesh's data model does not directly measure lexical change, but instead measures the results of lexical change, given that its results surface in the distribution of cognate sets across lexicostatistical word-lists. While historical linguists mostly focused on sound change processes before, often ignoring morphological and semantic change, the lexicostatistical data model moved semantic change, lexical borrowing, and (to a lesser degree also) morphological change into the spotlight of linguistic endeavors. As an example, consider the following quote from Lees (1953), discussing the investigation of change in vocabulary under the label of morpheme decay:

The reasons for morpheme decay, ie. for change in vocabulary, have been classified by many authors; they include such processes as word tabu, phonemic confusion of etymologically distinct items close in meaning, change in material culture with loss of obsolete terms, rise of witty terms or slang, adoption of prestige forms from a superstratum language, and various gradual semantic shifts such as specialization, generalization, and pejoration. [Lees 1953: 114]

In addition to lexicostatistics and the discussions that arose especially from it (including those that criticized the method harshly), I consider the aforementioned model of three dimensions of lexical change by Gevaudán (2007) to be very useful in this context, since it constitutes one of the few attempts to approach the question of lexical change in a formal (or formalizable) way.

Computational approaches

Among the most frequently used models in the historical linguistics literature are those in which lexical change is modeled as a process of cognate gain and cognate loss. Modeling lexical change as a process of word gain and word loss, or root gain and root loss, is in fact straightforward. We well know that languages may cease to use certain words during their evolution, either because the things the words denote no longer exist (think of the word walkman and then try to project the future of the word ipad), or because a specific word form is no longer being used to denote a concept and therefore drops out of the language at some point (think of thorp which meant something like "village", as a comparison with German Dorf "village" shows, but now exists only as a suffix in place names).

Since the gain-loss (or birth-death) model finds a direct counterpart in evolutionary biology, where genome evolution is often modeled as a process involving gain and loss of gene families (Cohen et al. 2008), it is also very easy to apply it to linguistics. The major work on the stochastic description of different gain-loss models has already been done, and we can find very stable software to helps us employ gain-loss models to reconstruct phylogenetic trees (Ronquist and Huelsenbeck 2003).

It is therefore not surprising that gain-loss models are very popular in computational approaches to historical linguistics. Starting from pioneering work by Gray and Jordan (2000) and Gray and Atkinson (2003), they have now been used on many language families, including Austronesian (Gray et al. 2007), Australian languages (Bowern and Atkinson 2012), and most recently also Sino-Tibetan (Sagart et al. 2019). Although scholars (including myself) have expressed skepticism about their usefulness (List 2016), the gain-loss model can be seen as reflecting the quasi-standard of phylogenetic reconstruction in contemporary quantitative historical linguistics.

Despite their popularity for phylogenetic reconstructions, gain-loss models have been used only sporadically in simulation studies. The only attempts that I know of so far are one study by Greenhill et al. (2009), where the authors used the TraitLab software (Nicholls 2013) to simulate language change along with horizontal transfer events, and a study by Murawaki (2015), in which (if I understand the study correctly) a gain-loss model is used to model language contact.

Another approach is reflected in the more "classical" work on lexicostatistics, where lexical change is modeled as a process of lexical replacement within previously selected concept slots. I will call this model the concept-slot model. In this model (and potential variants of it), a language is not a bag of words whose contents changes over time, but is more like a chest of drawers, in which each drawer represents a specific concept and the content of a drawer represents the words that can be used to express that given concept. In such a model, lexical change proceeds as a replacement process: a word within a given concept drawer is replaced by another word.

This model represents the classical way in which Morris Swadesh used to view the evolution of a given language. It is still present in the work of scholars working in the original framework of lexicostatistics (Starostin 2000), but it is used almost exclusively within distance-based frameworks, since a character-based account of the model would require a potentially large number of character states, which usually exceeds the number of character states allowed in the classical software packages for phylogenetic reconstruction.

Similar to the gain-loss model, there have not been many attempts to test the characteristics of this model in simulation studies. The only one known to me is a posthumously published letter from Sergei Starostin (1953-2005) to Murray Gell-Mann (Starostin 2007), in which he describes an attempt to account for his theory that a word's replacement rage increases with the word's age (Starostin 2000) in a computer simulation.

Problems with current models of lexical change

Neither the gain-loss model nor the concept-slot model seem to be misleading when it comes to describe the process of lexical change. However, they both obviously ignore specific and crucial aspects of lexical change that (according to the task stated above) any ambitious simulation of lexical change should try to account for. The gain-loss model, for example, deliberately ignores semantic change and morphological change. It can account for borrowings, which can be easily included in a simulation by allowing contemporary languages to exchange words with each other, but it cannot tell us (since it ignores the meaning of word forms) how the meaning of words changes over time, or how word forms change their shape due to morphological change.

The concept-slot model can, in theory, account for semantic change, but only as far as the concept-slots allow: the number of concepts in this model is fixed and one usually does not assume that it would change. Furthermore, while borrowing can be included in this model, the model does not handle morphological change processes.

In phylogenetic approaches, both models also have clear disadvantages. The main problem of the gain-loss model is the sampling procedure. Since one cannot sample all words of a language, scholars usually derive the cognate sets they use to reconstruct phylogenies from cognate-coded lexicostatistical word-lists. As I have tried to show earlier, in List (2016), this sampling procedure can lead to problems when homology is defined in a loose way. The problem of the concept-slot model is that it cannot be easily applied in phylogenetic inference based on likelihood models (like Maximum likelihood or Bayesian inference), since the only straightforward way to handle them would be multi-state models, which are generally difficult to handle.

Initial ideas for improvement

For the moment, I have no direct idea of how to model morphological change, and more research will be needed before we will be able to handle this in models of lexical change. The problem of the gain-loss and the concept-slot models to account for semantic change, however, can be overcome by turning to bipartite graph models of lexical change (see Newman 2010: 32f for details on bipartite graphs). In such a model, the lexicon of a human language is represented by a bipartite graph consisting of concepts as one type of node and word forms (or forms) as another type of node. The association strength of a given word node and a given concept node (or its "reference potential", see List 2014: 21f), ie. the likelihood of a word being used by a speaker to denote a given concept, can be modeled with help of weighted edges. This model naturally accounts for synonymy (if a meaning can be expressed by multiple words) and polysemy (if a word can express multiple meanings). Lexical change in such a model would consist of the re-arrangement of the weights in the network. Word loss and word gain would occur if a new word node is introduced into the network or an existing node gets dissociated from all of the concepts.

Sankoff's (1996) bipartite model of the lexicon of human languages

We can find this idea of bipartite modeling of a language's lexicon in the early linguistic work of Sankoff (1969: 28-53), as reflected in the figure above, taken from his dissertation (Figure 5, p. 36). Similarly, Smith (2004) used bipartite form-concept networks (which he describes as a matrix) in order to test the mechanisms by which these vocabularies are transmitted from the perspective of different theories on cultural evolution.

As I have never actively tried to review the large amount of literature devoted to simulation studies in historical linguistics, biology, and cultural evolution, it is quite possible that this blogpost lacks reference to important studies devoted to the problem. Despite this possibility, we can clearly say that we are lacking simulation studies in historical linguistics. I am furthermore convinced that the problem of handling lexical change in simulation studies is a difficult one, and that we may well have to wait to acquire more knowledge of the key processes involving lexical change in order to address it sufficiently in the future.

While I understand the popularity of gain-loss models in recent work on phylogenetic reconstruction in historical linguistics, I hope that it might be possible to develop more realistic models in the future. It is well possible that such studies will confirm the superiority of gain-loss models over alternative approaches. But instead of assuming this in an axiomatic way, as we seem to be doing it for the time being, I would rather see some proof for this in simulation studies, or in studies where the data fed to the gain-loss algorithms is sampled differently.

References

Bowern, Claire and Atkinson, Quentin D. (2012) Computational phylogenetics of the internal structure of Pama-Nguyan. Language 88: 817-845.

Cohen, Ofir and Rubinstein, Nimrod D. and Stern, Adi and Gophna, Uri and Pupko, Tal (2008) A likelihood framework to analyse phyletic patterns. Philosophical Transactions of the Royal Society B 363: 3903-3911.

Gévaudan, Paul (2007) Typologie des lexikalischen Wandels. Bedeutungswandel, Wortbildung und Entlehnung am Beispiel der romanischen Sprachen. Tübingen:Stauffenburg.

Gray, Russell D. and Jordan, Fiona M. (2000) Language trees support the express-train sequences of Austronesian expansion. Nature 405: 1052-1055.

Gray, Russell D. and Atkinson, Quentin D. (2003) Language-tree divergence times support the Anatolian theory of Indo-European origin. Nature 426: 435-439.

Gray, Russell D. and Greenhill, Simon J. and Ross, Malcolm D. (2007) The pleasures and perils of Darwinzing culture (with phylogenies). Biological Theory 2: 360-375.

Greenhill, S. J. and Currie, T. E. and Gray, R. D. (2009) Does horizontal transmission invalidate cultural phylogenies? Proceedings of the Royal Society of London, Series B 276: 2299-2306.

Lees, Robert B. (1953) The basis of glottochronology. Language 29: 113-127.

List, Johann-Mattis (2016) Beyond cognacy: Historical relations between words and their implication for phylogenetic reconstruction. Journal of Language Evolution 1: 119-136.

Murawaki, Yugo (2015) Spatial structure of evolutionary models of dialects in Contact. PLoS One 10: e0134335.

Newman, M. E. J. (2010) Networks: An Introduction. Oxford: Oxford University Press.

Nicholls, Geoff K and Ryder, Robin J and Welch, David (2013) TraitLab: A MatLab package for fitting and simulating binary tree-like data.

Ronquist, Frederik and Huelsenbeck, J. P. (2003) MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19: 1572–1574.

Sagart, Laurent, Jacques, Guillaume, Lai, Yunfan, Ryder, Robin, Thouzeau, Valentin, Greenhill, Simon J., List, Johann-Mattis (2019) Dated language phylogenies shed light on the ancestry of Sino-Tibetan. Proceedings of the National Academy of Science of the United States of America 116: 10317–10322. DOI: 10.1073/pnas.1817972116

Sankoff, David (1969) Historical Linguistics as Stochastic Process. McGill University: Montreal.

Smith, Kenny (2004) The evolution of vocabulary. Journal of Theoretical Biology 228: 127-142.

Starostin, Sergej Anatolévič (2000) Comparative-historical linguistics and lexicostatistics. In: Renfrew, Colin, McMahon, April, Trask, Larry (eds.): Time Depth in Historical Linguistics: 1. Cambridge:McDonald Institute for Archaeological Research, pp. 223-265.

Starostin, Sergej A. (2007) Computer-based simulation of the glottochronological process (Letter to M. Gell-Mann). In: : S. A. Starostin: Trudy po yazykoznaniyu [S. A. Starostin: Works in Linguistics]. LRC Publishing House, pp. 854-861.

Swadesh, Morris (1952) Lexico-statistic dating of prehistoric ethnic contacts. With special reference to North American Indians and Eskimos. Proceedings of the American Philosophical Society 96: 452-463.

Swadesh, Morris (1955) Towards greater accuracy in lexicostatistic dating. International Journal of American Linguistics 21.2: 121-137.

Controversies about structural data in historical linguistics

In the past, there have been many controversies about structural data, — that is, the kind of that data I introduce in the post written last month. Given the misinterpretation of structural data as being "grammatical", along with the unproven and misleading claim by Nichols (2003) that certain grammatical features are more stable than lexical ones, one can often read about a controversy in linguistics: which aspects are more stable, and therefore more useful to study deep linguistic relationships, the lexicon or the grammar?

In this context, it is often ignored that we are not talking chiefly about the grammar when applying phylogenetic studies to structural datasets. It is also ignored that the original idea of the importance of "grammar" was pointing to homologies in complex and concrete morphological paradigms, as has been most prominently discussed by Meillet (1925), later popularized by Nichols (1996) (i.e., individual word forms, that is: predominantly lexical traits). "Grammar" never pointed to abstract similarities as they are captured in most structural datasets (see the excellent discussion by Dybo and Starostin).

"Grammar" as evidence for deep language relations

Leading scholars in historical linguistics have provided convincing arguments that genetic relationships among languages can only be demonstrated by illustrating regular sound correspondences in concrete form-meaning pairs across the languages under investigation (see especially the very good analysis by Campbell and Poser 2008). In spite of this, the rumor that "grammar" (i.e., structural datasets) might provide a shortcut to detect deep, so far unnoticed, relationships among the languages of the world is very persistent, as reflected in many different studies.

Among the examples, Dunn et al. (2008) claimed that language relationships for Papuan languages of Island Melanesia could be uncovered by means of phonological and grammatical (abstract) structural features; and Longobardi et al. (2015) used syntactic features to compare the development of European languages with the development of European populations. Zhang et al. (2018) used phonological inventories of more than 100 different Chinese dialects, coding the data for simple presence and absence of each of the more than 200 different sounds in the database, and analyzing the data with the STRUCTURE software (Pritchard et al. 2000), whose results tend to be notoriously misinterpreted.

What is important about these studies is that none of them (maybe with exception of the study by Dunn et al. 2008, but I am in no position to actually judge the findings) could make a convincing claim why the structural datasets would provide evidence of deeper relationships than could the lexicon. Even the study by Dunn et al., which tests the suitability of their small questionnaire of only 115 structural traits on Oceanic languages, has since then not led to any new insights into so far undetected language relationships, contrary to the hope expressed by the authors, "that structural phylogeny is an important new tool for exploring historical relationships between languages" (ibid. 734).

Structural data as a shortcut?

Some scholars who work on structural datasets may find my claims harsh and unjustified. In fact, there are studies that seem to provide evidence that structural datasets perform similarly or equally well compared to phylogenetic methods based on lexical data.

For example, Longobardi et al.(2016) carry out experiments on structural data of phoneme inventories, syntactic features, and "traditional" cognate sets for very small Indo-European datasets, concluding that all of the datasets yield similar results, and that syntactic or phonological features in structural datasets could be used instead of lexical phylogenies.

Contrary to this, Grennhill et al. (2017) also experiment on lexical datasets in comparison with structural data for 81 Austronesian languages, but they find that, in general, lexical data is much more stable than structural data, although some structural features seem to be similar to lexical items regarding their stability.

A wish list for future tests

I see two major problems in the debate about the usefulness of structural data in historical linguistics.

First, the studies that confirm that structure might work equally well compared with lexical data, are all based on small samples of one specific language family that was analyzed based on very diverse features that were specifically designed to study the languages under question. For me, a true test that some features carry deep historical signal would need to be illustrated for a large set of related and unrelated languages, not only just for selected datasets.

Furthermore, to allow for an honest comparison with the lexicon, the selection of features should not contain any lexical characters or characters that could only be extracted with the help of lexical characters. Thus, asking whether the words for "fish", "I", and "five" are pronounced similarly in a language would not be allowed in such a feature collection, because this would follow lexical criteria, and we know very well that this property is a very good proxy for identifying Sino-Tibetan languages (Handel 2008).

Second, and more problematic, is the fact that structural datasets do not provide information on the relatedness of the traits under comparison. While this is no problem for typologists who study shared structural features out of interest in universal tendencies in the languages of the world, it is a problem for the application of phylogenetic software, since the typical approaches in biology treat homoplasy as an exception, while it may often be rather the norm than an exception in structural datasets.

Conclusion

In order to make structural data suitable for historical analyses, much more research needs to be carried out, including specifically a much thorougher study of parallel evolution and geographic convergence (due to language contact) in different language families of the world — a nice illustration for the Indo-European languages is provided by Cathcard et al. (2018).

I would be happy for our field if such research could reveal markers of deep genetic ancestry in the languages of the world, and help us to push the boundaries of linguistic reconstruction. For the time being, however, I remain highly skeptical, especially when scholars try to demonstrate the suitability of "grammatical" comparison with small datasets and idiosyncratically selected feature sets that are not comparable across datasets.

References

Campbell, L. and W. Poser (2008) Language Classification: History and Method. Cambridge University Press: Cambridge.

Cathcard, C., G. Carling, F. Larson, R. Johansson, and E. Round (2018) Areal pressure in grammatical evolution. An Indo-European case study. Diachronica 35.1: 1-34.

Dunn, M., S. Levinson, E. Lindstroem, G. Reesink, and A. Terrill (2008) Structural phylogeny in historical linguistics: methodological explorations applied in island melanesia. Language 84.4. 710-759.

Dybo, A. and G. Starostin (2008) In defense of the comparative method, or the end of the Vovin controversy. In: Smirnov, I. (ed.) Aspekty komparativistikiAspekty komparativistiki.3. RGGU: Moscow, pp 119-258.

Greenhill, S., C. Wu, X. Hua, M. Dunn, S. Levinson, and R. Gray (2017) Evolutionary dynamics of language systems. Proceedings of the National Academy of Sciences 114.42: E8822-E8829.

Handel, Z. (2008) What is Sino-Tibetan? Snapshot of a field and a language family in flux. Language and Linguistics Compass 2.3: 422-441.

Longobardi, G., S. Ghirotto, C. Guardiano, F. Tassi, A. Benazzo, A. Ceolin, and G. Barbujan (2015) Across language families: Genome diversity mirrors linguistic variation within Europe. American Journal of Physical Anthropology 157.4.: 630-640.

Longobardi, G., A. Buch, A. Ceolin, A. Ecay, C. Guardiano, M. Irimia, D. Michelioudakis, N. Radkevich, and G. Jaeger (2016) Correlated Evolution Or Not? Phylogenetic Linguistics With Syntactic, Cognacy, And Phonetic Data. In: The Evolution of Language: Proceedings of the 11th International Conference (EVOLANGX11).

Meillet, A. (1954) La méthode comparative en linguistique historique [The comparative method in historical linguistics]. Honoré Champion: Paris.

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

Nichols, J. (2003) Diversity and stability in language. In: Joseph, B. and R. Janda (eds.) The Handbook of Historical Linguistics. Blackwell: Malden, Mass, pp 283-310.

Pritchard, J., M. Stephens, and P. Donnelly (2000) Inference of population structure using multilocus genotype data. Genetics 155: 945–959.

Structural data in historical linguistics

The majority of historical linguists compare words to reconstruct the history of different languages. However, in phylogenetic studies focusing on cognate sets reflecting shared homologs across the languages under investigation, there exists another data type that people have been trying to explore in the past. The nature of this data type is difficult to understand for non-linguists, given that it has a very abstract nature. In the past, it has led to a considerable amount of confusion both among linguists and among non-linguists who tried to use this data for quick (and often also dirty) phylogenetic approaches. For this reason, I figured it would be useful to introduce this type of data in more detail.

This data type can be called "structural". To enable interested readers to experiment with the data themselves, this blogpost comes along with two example datasets that we converted into a computer-readable format (with much help from David), since the original papers only offered the data as PDF files. In future blogposts, we will try to illustrate how the data can, and should, be explored with network methods. In this first blogpost, I will try to explain the basic structure of the data.

Structural data in historical linguistics and language typology

In order to illustrate the type of data we are dealing with here, let's have a look at a typical dataset, compiled by the famous linguist Jerry Norman to illustrate differences between Chinese dialects (Norman 2003). The table below shows a part of the data provided by Norman.

No.	Feature	Beijing	Suzhou	Meixian	Guangzhou
1	The third person pronoun is tā, or cognate to it	+	-	-	-
4	Velars palatalize before high-front vowels	+	+	-	-
7	The qu-tone lacks a register distinction	+	-	+	-
12	The word for "stand" is zhàn or cognate to it	+	-	-	-

In this example, the data is based on a questionnaire that provides specific questions; and for each of the languages in the sample, the dataset answers the question with either + or -. Many of these datasets are binary in their nature, but this is not a necessary condition, and questionnaires can also query categorical variables, such as, for example, the major type of word order might have three categories (subject-object-verb, subject-verb-object or other).

We can also see is that the questions can be very diverse. While we often use more or less standardized concept lists for lexical research (such as fixed lists of basic concepts, List et al. 2016), this kind of dataset is much less standardized, due to the nature of the questionnaire: asking for the translation of a concept is more or less straightforward, and the number of possible concepts that are useful for historical research is quite constrained. Asking a question about the structure of a language, however, be it phonological, lexical, based on attested sound changes, or on syntax, provides an incredible number of different possibilities. As a result, it seems that it is close to impossible to standardize these questions across different datasets.

Although scholars often call the data based on these questionnaires "grammatical" (since many questions are directed towards grammatical features, such as word order, presence or absence of articles, etc.), most datasets show a structure in which questions of phonology, lexicon, and grammar are mixed. For this reason, it is misleading to talk of "grammatical datasets", but instead the term "structural data" seems more adequate, since this is what the datasets were originally designed for: to investigate differences in the structure of different languages, as reflected in the most famous World Atlas of Language Structures (Dryer and Haspelmath 2013, https://wals.info).

Too much freedom is a restriction

In addition to mixed features that can be observed without knowing the history of the languages under investigation, many datasets (including the one by Norman we saw above) also use explicit "historical" (diachronic in linguistic terminology) questions in their questionnaires. In his paper describing the dataset, Norman defends this practice, as he argues that the goal of his study is to establish an historical classification of the Chinese dialects. With this goal in mind, it seems defensible to make use of historical knowledge and to include observed phenomena of language change in general, and sound change in specific, when compiling a structural dataset for group of related language varieties.

The problem of the extremely diverse nature of questionnaire items in structural datasets, however, makes their interpretation extremely difficult. This becomes especially evident when using the data in combination with computational methods for phylogenetic reconstruction. This is problematic for two major reasons.

1. Since questions are by nature less restricted regarding their content, scholars can easily pick and choose the features in such a way that they confirm the theory they want them to confirm rather than testing it objectively. Since scholars can select suitable features from a virtually unlimited array of possibilities, it is extremely difficult to guarantee the objectivity of a given feature collection. 
2. If features are mixed, phylogenetic methods that work on explicit statistical models (like gain and loss of character states, etc.) may often be inadequate to model the evolution of the characters, especially if the characters are historical. While a feature like "the language has an article" may be interpreted as a gain-loss process (at some point, the language has no article, then it gains the article, then it looses it, etc.), features showing the results of processes, like "the words that originally started in [k] followed by a front vowel are now pronounced as [tɕ]", cannot be interpreted as a process, since the feature itself describes a process.

For these reasons, all phylogenetic studies that make use of structural data, in contrast to purely lexical datastes, should be taken with great care, not only because they tend to yield unreliable results, but more importantly because they are extremely difficult to compare across different language families, given that they have way too much freedom when compiling them. Feature collections provided in structural datasets are an interesting resource for diversity linguistics, but they should not be used to make primary claims about external language history or subgrouping.

Two structural datasets for Chinese dialects

Before I start to bore the already small circle of readers interested in these topics, it seems better to stop discussing the usefulness of structural data at this point, and to introduce the two datasets that were promised at the beginning of the post.

Both datasets target Chinese dialect classification, the former being proposed by Norman (2003), and the latter reflecting a new data collection that was recently used by Szeto et al. (2018) to propose a North-South-split of dialects of Mandarin Chinese with help of a Neighbor-Net analysis (Bryant and Moulton 2004). Both datasets have been uploaded to Zenodo, and can be found in the newly established community collection cldf-datasets. The main idea of this collection is to collect various structural datasets that have been published in the literature in the past, and allow those people interested in the data, be it for replication studies or to thest alternative approaches, easy access to the data in various formats.

The basic format is based on the format specifications laid out by the CLDF initiative (Forkel et al. 2018), which provides a software API, format specifications, and examples for best practice for both structural and lexical datasets in historical linguistics and language typology. The collection is curated on GitHub (cldf-datasets), and datasets are converted to CLDF (with all languages being linked to the Glottolog database, glottolog.org, Hammarström et al. 2018) and also to Nexus format. The dataset is versionized, it may be updated in the future, and interested readers can study the code used to generate the specific data format from the raw files, as well as the Nexus files, to learn how to submit their own datasets to our initiative.

Final remarks on publishing structural datasets online

By providing only two initial datasets for an enterprise whose general usefulness is highly questionable, readers might ask themselves why we are going through the pain of making data created by other people accessible through the web.

The truth is that the situation in historical linguistics and language typology has for a very long time been very unsatisfactory. Most of the research based on data did not supply the data with the paper, and often authors directly refuse to share the data when asked after publication (see also the post on Sharing supplementary data). In other cases, access to the data is exacerbated by providing data only in PDF format in tables inside the paper (or even worse: long tables in the supplement of a paper), which force scholars wishing to check a given analysis themselves to reverse-engineer the data from the PDF. That data is provided in a form difficult to access is not even necessarily the fault of the authors, since some journals even restrict the form of supplementary data to PDF only, giving authors wishing to share their data in an appropriate form a difficult time.

Many colleagues think that it is time to change this, and we can only change it by offering standard ways to share our data. The CLDF along with the Nexus file, in which the two Chinese datasets are now published in this open repository collection, may hopefully serve as a starting point for larger collaboration among typologists and historical linguistics. Ideally, all people who publish papers that make use of structural datasets, would — similar to the practice in biology where scholars submit data to GenBank (Benson et al. 2013) — submit their data in CLDF format and Nexus, so that their colleagues can easily build on their results, and test them for potential errors.

References

Benson D., M. Cavanaugh, K. Clark, I. Karsch-Mizrachi, D. Lipman, J. Ostell, and E. Sayers (2013) GenBank. Nucleic Acids Res. 41.Database issue: 36-42.

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.
Campbell, L. and W. Poser (2008): Language classification: History and method. Cambridge University Press: Cambridge.

Cathcard C., G. Carling, F. Larson, R. Johansson, and E. Round (2018) Areal pressure in grammatical evolution. An Indo-European case study. Diachronica 35.1: 1-34.

Dryer M. and Haspelmath, M. (2013) WALS Online. Max Planck Institute for Evolutionary Anthropology: Leipzig.

Forkel R., J.-M. List, S. Greenhill, C. Rzymski, S. Bank, M. Cysouw, H. Hammarström, M. Haspelmath, G. Kaiping, and R. Gray (forthcoming) Cross-Linguistic Data Formats, advancing data sharing and re-use in comparative linguistics. Scientific Data.

Hammarström H., R. Forkel, and M. Haspelmath (2018) Glottolog. Version 3.3. Max Planck Institute for Evolutionary Anthropology: Leipzig. http://glottolog.org.

List J.-M., M. Cysouw, and R. Forkel (2016) Concepticon. A resource for the linking of concept lists. In: Proceedings of the Tenth International Conference on Language Resources and Evaluation, pp 2393-2400.

Norman J. (2003) The Chinese dialects. Phonology. In: Thurgood, G. and R. LaPolla (eds.) The Sino-Tibetan languages. Routledge: London and New York, pp 72-83.

Pritchard J., M. Stephens, and P. Donnelly (2000) Inference of population structure using multilocus genotype data. Genetics 155: 945–959.

Szeto P., U. Ansaldo, and S. Matthews (2018) Typological variation across Mandarin dialects: An areal perspective with a quantitative approach. Linguistic Typology 22.2: 233-275.

Zhang M., W. Pan, S. Yan, and L. Jin (2018) Phonemic evidence reveals interwoven evolution of Chinese dialects. bioarxiv.

Horizontal and vertical language comparison

In the traditional handbooks on historical language comparison, one can often find the claim that there are two fundamentally different, but equally important, means of linguistic reconstruction. One is usually called "external reconstruction" (or alternatively the "comparative method"), and one is called "internal reconstruction". If we think of sequence comparison in historical linguistics in the form of a table, in which concepts are arranged on the vertical axis, and different languages on the horizontal axis, we can look at the two different modes of language comparison (external vs. internal) as the horizontal and the vertical axes of the table. Horizontal language comparison refers to external reconstruction — scholars compare forms (not necessarily of the same meaning) across the horizontal axis, that is, across different languages. Internal language comparison is vertical — scholars search inside one and the same language for structures that allow to infer its older stages.

In past blog posts I have been talking a lot about horizontal / external language comparison, for which especially the notion of sound correspondences is crucial. But in the same way in which we use the evidence across languages to infer the past states of a given language family, we can make use of language-internal evidence to learn more about the history — not only of a given language,- but also of a group of languages.

Vertical Language Comparison

A classical example of vertical or internal language comparison is the investigation of paradigms, that is, the inflection systems of the verbs or nouns in a given language. This, of course, makes sense only if the respective languages have verbal or nominal morphology, ie. if we find differences in the verb forms for the first, second, or third person singular or plural, or for the case system. The principle would not work in Chinese, although we have different means to compare languages without inflection vertically, as I'll illustrate below.

As a simplified example of internal reconstruction, consider the verbal paradigm of the verb esse "to be" in Latin:

Person	Singular	Plural
first	sum	sumus
second	es	estis
third	est	sunt

If you try to memorize this pattern, you will quickly realize that it is not regular, and you will have difficulties to identify patterns that assist in memorizing the forms. A much more regular pattern would be the following:

Person	Singular	Plural
first	es-um	es-umus
second	es-Ø	es-tis
third	es-t	es-unt

This pattern would still require us to memorize six different endings, but we could safely remember that the beginning of all forms is the same, and that there are six different endings, accounting for person and number at the same time (which is anyway typical for inflecting languages).

An alternative pattern that would be easier to remember is the following one:

Person	Singular	Plural
first	es-um	s-umus
second	es-ø	s-tis
third	es-t	s-unt

While it may seem that this pattern is slightly more complicated at first glance, it would still be more regular than the pattern we actually observe, and we would now have two different aspects expressing the meaning of the different forms: the alternation of the root es- vs. s- accounts for the singular-plural distinction, while the endings express again both number and person.

If we look at older stages of Latin, we can, indeed, find evidence for the first person singular, which was written esom in ancient documents (see Meier-Brügger 2002 for details on the reconstruction of this paradigm in Indo-European). If we look at other languages, like Sanskrit and Ancient Greek, we can further see that our alternation between es- and s- in the root (thus our last example) comes also much closer to the supposed ancient state, even if we don't find complete evidence for this in Latin alone.

What we can see, however, is that the inspection of alternating forms of the same root can reveal ancient states of a language. The key assumption is that observed irregularities usually go back to formerly regular patterns.

Horizontal language comparison

The classical example for horizontal or external language comparison is the typical wordlists in which words with similar meanings across different languages are arranged in tabular form. I have mentioned before that it was in great part Morris Swadesh (1909-1967) who popularized the simple tabular perspective that puts a concept and its various translations in the center of historical language comparison. Before the development of this concept-based approach to historical linguistics, scholars would pick examples based on their similarity in form, allowing for great differences in the semantics of the words being assigned to the same slot of cognate words; and this exclusively form-based approach to external language comparison is still the prevalent one in most branches of historical linguistics.

No matter what approach we employ in this context — be it the concept- or the form-based — as long as we compare forms across different languages, we carry out external language comparison, and our main concern is then the identification of regular sound correspondences across the languages in our sample, which enable us to propose ancestral sounds for the ancestral language.

Problems of vertical language comparison

As can be seen from my above example of the inflection of esse in Latin, it is not obvious how the task of internal language comparison could be formalized and automated. There are two main reasons for this. First, inflection paradigms vary greatly among the languages of the world, which makes it difficult to come up with a common way to investigate them.

Second, since we are usually looking for irregular cases that we try to explain as having evolved from former regularities, it is clear that our data will be extremely sparse. Often, it is only the paradigm of one word that we seek to explain, as we have seen for Latin esse, and patterns of irregularities across many verbs are rather rare (although we can also find examples for this). As a result, internal reconstruction is dealing with even fewer data than external reconstruction, where data are also not necessarily big.

Formalizing the language-internal analysis of word families

Despite the obvious problems of exploiting the language-internal perspective in historical language comparison, there are certain types of linguistic analysis that are amenable to a more formal treatment in this area. One example that we are currently testing is the inference and annotation of word families within a given language. It is well known that large number of words in human languages are not unrelated atomic units, but have themselves been created from smaller parts. Linguists distinguish derivation and compounding as the major techniques here, by which new words are created from existing ones.

Derivation refers to those cases where a word is being modified by a form unit that could not form a word of its own, usually a suffix or a prefix. As an example, consider the suffix -er in English which can be attached to verbs in order to form a noun that usually describes the person that regularly carries out the action denoted by the original verb (eg. examine → examiner, teach → teacher, etc.). While the original verb form exists without the suffix in the English language, the form -er only occurs as part of verbs. In contrast to derivation, compounding refers to the process by which two word forms that can be used in isolation are merged to form a new expression (compare foot and ball with football).

Searching for suffixes and compounds in unannotated language data is a very difficult task. Although scholars have been working on automatic methods that split a given monolingual dictionary into its smallest meaning-bearing form units (morphemes), these methods usually only work on very large datasets (Creutz and Laugs 2005). Trained linguists, on the other hand, can easily detect patterns, even when working on smaller datasets of a few hundred words.

The reason why linguists are successful in analysing the morphology of languages, in contrast to machine-learning approaches, is that they make active use of their external knowledge about the potential semantics underlying the patterns, while current methods for automatic morpheme detection usually only consider the forms, and disregard the semantics. Semantics, however, are important to distinguish words that form a true family (in that they share cognate material) from words that are similar only due to chance.

It is clear that languages may have words that sound alike but convey different meanings. As an extreme example, consider French paix [pɛ] "peace" vs. pet [pɛ] "fart".Although both words are pronounced the same, we know that they are not cognate, going back to different ancestral forms, as is also reflected in the French writing system. But even if we lacked the evidence of the French orthography, we could easily justify that the words do not form a family, since (a) their meaning is quite different, and (b) their genus is different as well (la paix vs. le pet). An automatic method that disregards semantics and external evidence (like the orthography or the gender of nouns in our case) cannot distinguish words that are similar due to chance from words that are similar due to their history.

As a further example illustrating the importance of semantics, consider the data for Achang, a Burmish language, spoken in Myanmar (data from Huáng 1992), which is shown in the following graphic (derived from the EDICTOR tool and analyzed by Nathan W. Hill).

Word families in Achang, a Burmish language.

In this figure, we can see six words which all share tɕʰi⁵⁵ (high numbers represent tones) as their first part. As we can see from the detailed analysis of these compounds in Achang, which is given in the column "MORPHEMES" in the figure, our analysis claims that the form tɕʰi⁵⁵, which expresses the concepts "foot" or "leg" in isolation, recurs in the words for "hoof", "claw", "knee", and "thigh", but not in the word for ""ant". While the semantic commonalities among the former are plausible, as they all denote body parts which are closely related to "feet" or "legs", we do not find any transparent motivation for why the speakers should have used a compound containing the word for "foot" to denote an ant. Although we cannot demonstrate this at this point, we are hesitant to add the Achang word for "ant" to the word family based on compounds containing the word for "foot".

Bipartite networks of word families

For the time being, we cannot automate this analysis, since we lack data for the testing and training of potential algorithms. We can, however, formalize it in a very straightforward way: with help of a bipartite network (see Hill and List 2017). Bipartite networks are networks with two kinds of nodes, which are usually thought of as representing different types. While we can easily assign different types to all nodes in any network we are dealing with, bipartite networks only allow us to link nodes of different types. In our bipartite network of word families, the first type of nodes represent the forms of the words, while the second type represent the meanings attributed to the sub-parts of the words. In the figure above, the former can be found in the column "tokens", where the symbol "+" marks the boundaries, and the latter can be found in the column "MORPHEMES".

The following figure shows the bipartite network underlying the word family relations following from our analysis of words built with the morpheme "foot" in Achang.

Bipartite network of word families: nodes in red text represent the (reconstructed) meaning of the morphemes, and blue nodes the words in which those occur as parts.

Conclusion

The bipartite network above shows only a small part of the word family structure of one language, and the analysis and formalization of word families with help of bipartite networks thus remains exemplary and anecdotal. I hope, however, that the example illustrates how important it is to keep in mind that language change is not only about sound shifts that can be analyzed with help of language-external, horizontal comparison. Investigating the vertical (the language-internal) perspective of language evolution is not only fascinating, offering many so far unresolved methodological problems, it is at least as important as the horizontal perspective for a proper understanding of the dynamics underlying language change.

References

Creutz M. and Lagus K. (2005) Unsupervised morpheme segmentation and morphology induction from text corpora using Morfessor 1.0. Helsinki University of Technology, 2005, 81.

Hill N. and List J.-M. (2017) Challenges of annotation and analysis in computer-assisted language comparison: A case study on Burmish languages. Yearbook of the Poznań Linguistic Meeting 3.1. 47–76.

Meier-Brügger M. (2002) Indogermanische Sprachwissenschaft. de Gruyter: Berlin.

Huáng Bùfán 黃布凡 (1992) Zàngmiǎn yǔzú yǔyán cíhuì [A Tibeto-Burman lexicon]. Zhōngyāng Mínzú Dàxué 中央民族大学 [Central Institute of Minorities]: Běijīng 北京.