Sequence comparison in computational historical linguistics. Issue 2 (6th July 2018)

• Record Type: Journal Article
• Authors: List, Johann-Mattis; Walworth, Mary; Greenhill, Simon J; Tresoldi, Tiago; Forkel, Robert
• Is Part Of: Journal of language evolution. Volume 3:Issue 2(2018)
• Page Start: 130
• Page End: 144
• Publication Date: 2018-07-06
• Subjects: historical linguistics -- computer-assisted language comparison -- Polynesian languages -- cognate detection -- phonetic alignment; Linguistic change -- Periodicals; Historical linguistics -- Periodicals; Linguistics -- Periodicals; 417.705
• DOI: 10.1093/jole/lzy006
• Languages: English
• ISSNs: 2058-4571

Abstract

Abstract: With increasing amounts of digitally available data from all over the world, manual annotation of cognates in multi-lingual word lists becomes more and more time-consuming in historical linguistics. Using available software packages to pre-process the data prior to manual analysis can drastically speed-up the process of cognate detection. Furthermore, it allows us to get a quick overview on data which have not yet been intensively studied by experts. LingPy is a Python library which provides a large arsenal of routines for sequence comparison in historical linguistics. With LingPy, linguists can not only automatically search for cognates in lexical data, but they can also align the automatically identified words, and output them in various forms, which aim at facilitating manual inspection. In this tutorial, we will briefly introduce the basic concepts behind the algorithms employed by LingPy and then illustrate in concrete workflows how automatic sequence comparison can be applied to multi-lingual word lists. The goal is to provide the readers with all information they need to (1) carry out cognate detection and alignment analyses in LingPy, (2) select the appropriate algorithms for the appropriate task, (3) evaluate how well automatic cognate detection algorithms perform compared to experts, and (4) export their data into various formats useful for additional analyses or data sharing. While basic knowledge of the Python language is useful for all analyses, our tutorial is structured in such a way that scholars with basic knowledge of computing can follow through all steps as well.