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- Göransson, Elisabet, et al.
(författare)
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Improved distance measures for “mixed-content miscellanies" : an adaptation for the collections of sayings of the desert fathers and mothers
- 2022
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Ingår i: Digital Scholarship in the Humanities. - : Oxford University Press. - 2055-7671 .- 2055-768X. ; 38:1, s. 127-150
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Tidskriftsartikel (refereegranskat)abstract
- Collections of sayings of the desert fathers and mothers are extant in manuscripts in many languages and are organized differently. They are ‘fixed-content miscellanies’ (FCM): they include material that belongs to the same genre, but is variable both when it comes to appearance and order. Distance measurement methods are particularly suitable for large text traditions including variable content in the so-called mixed-content miscellanies, such as recipes, anthological compilations of shorter text passages, or catalogues, but can also be suitable for text genres like collections of sayings, that are equally variable in appearance and order of sayings, even though the genre is fixed; hence ‘fixed-content miscellanies’. In the article, collections of sayings in seven languages were compared using four distance measures methods. Each segment of the sayings was given a unique id to be comparable. The first method used, the Jaccard distance measure, disregards the linear order of items and instead considers each collection compared only as a ‘bag of stories’. In two other methods used (Birnbaum and Levenshtein methods), the order in which the narratives of each saying appear is compared. All three methods yielded interesting results, but the collections that were apparently closely related were clustered together so tightly that it was not possible to make more nuanced analyses. In order to remove false negatives, particulars concerning lacunes in the material were taken into account in the proposed modified Levenshtein method, the fixed-content miscellanies (FCM)-Levenshtein method. By applying the FCM-Levenshtein method, previously unknown relations between collections witnessed in different languages could be detected.
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| 3. |
- Syrjånen, Kaj, et al.
(författare)
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Crouching TIGER, hidden structure : Exploring the nature of linguistic data using TIGER values
- 2021
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Ingår i: JOURNAL OF LANGUAGE EVOLUTION. - : Oxford University Press. - 2058-458X .- 2058-4571. ; 6:2, s. 99-118
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, techniques such as Bayesian inference of phylogeny have become a standard part of the quantitative linguistic toolkit. While these tools successfully model the tree-like component of a linguistic dataset, real-world datasets generally include a combination of tree-like and nontree-like signals. Alongside developing techniques for modeling nontree-like data, an important requirement for future quantitative work is to build a principled understanding of this structural complexity of linguistic datasets. Some techniques exist for exploring the general structure of a linguistic dataset, such as NeighborNets, delta scores, and Q-residuals; however, these methods are not without limitations or drawbacks. In general, the question of what kinds of historical structure a linguistic dataset can contain and how these might be detected or measured remains critically underexplored from an objective, quantitative perspective. In this article, we propose TIGER values, a metric that estimates the internal consistency of a genetic dataset, as an additional metric for assessing how tree-like a linguistic dataset is. We use TIGER values to explore simulated language data ranging from very tree-like to completely unstructured, and also use them to analyze a cognate-coded basic vocabulary dataset of Uralic languages. As a point of comparison for the TIGER values, we also explore the same data using delta scores, Q-residuals, and NeighborNets. Our results suggest that TIGER values are capable of both ranking tree-like datasets according to their degree of treelikeness, as well as distinguishing datasets with tree-like structure from datasets with a nontree-like structure. Consequently, we argue that TIGER values serve as a useful metric for measuring the historical heterogeneity of datasets. Our results also highlight the complexities in measuring treelikeness from linguistic data, and how the metrics approach this question from different perspectives.
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