Category Archives: Annotation

RhyAnT: A web-based tool for interactive rhyme annotation

In times where home office is an obligation rather than an option, I have finally found time to create a first draft version of a web-based tool for interactive rhyme annotation. The tool is written in plain JavaScript, without any additional libraries, and supports the inline rhyme annotation format which we proposed in an earlier study. It allows for an efficient and save annotation of poems for their rhyme structure and will hopefully help us to assemble larger samples of rhyme patterns across genres, languages, times, and cultures.

Continue reading

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.

 

Continue reading

Enhancing morphological annotation for internal language comparison

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).

Continue reading