Rooting of phylogenetic trees is an important task, not only in evolutionary biology, but also in historical linguistics. So far, however, different rooting methods have not yet been sufficiently tested on linguistics data. Given that a new method for the automatic rooting of phylogenetic trees has been presented recently in biology, it seemed to be a good occasion to test in detail how well this new method works in comparison with alternative methods.
Evolutionary biology and historical linguistics share a long history of scientific exchange, reflected both not only in the sharing and transfer of metaphors but more recently also in the transfer of methods. Already Charles Darwin claimed that both species and languages evolve in tree-like patterns, and linguists, like Wilhelm Meyer-Lübke, used terms like “sprachliche[n] Biologie” (‘linguistic biology’) when referring to the history of languages (Meyer-Lübke 1890, x). While the discipline of historical-comparative linguistics allowed biologists to adopt evolutionary thought against religious dogma in the late 19th century (Wells 1987: 54), it was biological applications which opened up the possibility of large quantitative studies in linguistics based on computational approaches (Geisler and List 2013, 111).
One of the major efforts behind our recently published paper on the origin and spread of the Sino-Tibetan languages (Sagart et al. 2019) was the creation of a database of lexical cognates which was used to run the phylogenetic analyses. The creation of this database started about four years ago, when I joined the Centre des Recherches Linguistiques sur l’Asie Oriental in Paris as a research fellow in January 2015, and Guillaume Jacques and Laurent Sagart approached me with the idea of making a phylogenetic study of Sino-Tibetan languages. In December 2017, almost three years after having started, our database consisted of 180 concepts translated into 50 different languages. Since creating the database was not directly straightforward from the beginning, with quite a few situations in which we realized we had to re-arrange the data or the procedure, I thought it might be useful to share our experience in a series of blog posts, as it might be interesting for scholars who wish to create their own database.
Mapping a given concept list to Concepticon can be done in a straight-forward way, even if automatic mappings need manual refinement. But what can we do when having to deal with larger datasets, say, a dictionary, from which we want to extract specific concepts, such as, for example, the ones in the classical Swadesh list of 100 items (Swadesh 1955)?
A major problem for data reuse in computer-assisted historical linguistics, especially when employing data collected with no computational workflows in mind, is linking datasets in terms of the meanings of the words (or, technically, “forms”) they carry. Just as linking languages across different datasets is not as straightforward as one might naively assume, demanding a complex reference catalog such as Glottolog, linking the concepts used in a wordlist (a “concept list”) to our Concepticon project might well be the most intensive task in preparing a dataset for cross-linguistic studies.
Having illustrated how a quick correspondence pattern analysis can be done with help of readily formatted data and the EDICTOR tool alone, it is now time to show how we can use the LingRex package in order to carry out a full-fledged correspondence pattern analysis. While EDICTOR uses a simple algorithm that is based on sorting the patterns, the Python algorithm for correspondence pattern detection, which is described in detail in List (2019), uses a greedy approach inspired by the Welsh-Powell algorithm for graph coloring (Welsh and Powell 1967), in order to cluster all alignment sites in the data into clusters which are compatible with each other.
Following up on my announcement to present in more detail how the algorithms for automatic correspondence pattern detection can be applied, this post introduces the preliminary preparations needed to run a first experiment with aligned data. In order to avoid that we have to align a dataset completely from scratch, we make use of already aligned data from the Tableaux Phonétiques des Patois Suisses Romands by Gauchat et al. (1925), which were originally aligned for the study in List (2014) and later published as part of the Benchmark Database of Phonetic Alignments(List and Prokić 2014). In this post, I will introduce how we can harvest the alignments from this dataset with help of LingPy, and later analyze them with help of the sound correspondence pattern algorithms.
After about three years of work on the matter, I have managed (with help of many colleagues who helped in testing) to develop a first approach for the automatic inference of sound correspondence patterns, which will soon be published with Computational Linguistics (List 2019). The key task which this algorithm solves is to take aligned data as input and to compute explicit sound correspondence patterns from the alignments.
Imagine you have two different datasets, both containing approximately the same concepts, but slightly different numbers of columns and — more importantly — potentially identical identifiers in the first column. A bad idea for merging these datasets would be to paste them in Excel or some other kind of spreadsheet software, and then trying to manually adjust all problems that might occur during this process.
A better idea is to just use LingPy and our CLDF curation framework Continue reading
In a previous post (Kaiping 2018), I described how to convert matrix-shape word lists given in Excel into the long format LingPy and other software can work with. My motivation for this was to provide my colleague Yunus Sulistyono with a good way to compare the lexicon of his Alorese [alor1247] dialects, and to understand the relationship between them. In this post, the data is automatically cognate coded and converted into CLDF. Continue reading
The blog post introduces ideas discussed in our project about taking a closer look at word formation from a semantic (or semasiological) point of view. Since this so far underinvestigated approach to word formation processes lacks proper terminology, a new term to denote the central research question of concept-based type-frequency is introduced and contrasted with related established terminology.
A colleague of mine has recently returned from his fieldwork, where he collected data on on the dialectal variation of the Alorese language of Alor and Pantar in the East Nusa Tenggara province of Indonesia. He collected data on 13 Alorese varieties, including word list data. One obvious step for comparing the dialects is to mark which forms are obviously cognate and then use a standard tree (or network) construction algorithm to display the shared signal in the data. With standard tools and a bit of Python glue, this is an easy task. A script for 3 steps can be found in my repository on github. In this first part, I will describe how to get an Excel file into a format LingPy can deal with.
LingPy (List et al. 2017) offers a great deal of functions for string manipulation. Although most of those functions are readily documented (see lingpy.org for details), and the basic ideas have also been described in my dissertation (List 2014), it seems that not many users are aware of these additional possibilities, which the library offers.
In the following, I want to illustrate how we can use LingPy to learn something about consonant clusters occurring in the data underlying the CLICS database (List et al. 2018, clics.clld.org). I have illustrated in an earlier post how one can use the CLICS software API to cook one’s own CLICS application. I will thus assume that you know how to install CLICS (following the instructions on our GitHub page) and the data underlying it.
In language comparison, there is a long history of using concept-based wordlists to get insights into the degree of similarity between languages, going back at least to Morris Swadesh (Swadesh 1950). For these purposes, words from different languages that share the same meaning are compared, either manually or with computational methods. The latter have the advantages of being both faster and more consistent. However, there are also limits to what computer-based methods can detect for the time being.
One of the biggest problems in this context is that none of the currently available methods for automatic cognate detection can infer partial cognates directly if no information on morpheme boundaries is provided by the user. As a result, if morpheme boundaries are missing and morphological differences are frequent in the data one wants to investigate, automatic cognate detection can be seriously hampered (List, Greenhill, and Gray 2017).