Using pyconcepticon to map concept lists (II)

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)?

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Using pyconcepticon to map concept lists

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.

 

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