Layers of variation

Subtitle:a computational approach to collating texts with revisions

The article describes research into the automatic comparison of texts with revisions. The authors argue that in-text variation can best be modelled as nonlinear text, and that a collation tool needs to treat in-text variation differently from the way linear text is treated. They describe in detail how the modelling choices they made influence the development of HyperCollate, a collation software that is able to process TEI-XML transcriptions of texts with variation. Consequently, HyperCollate produces a more refined collation output that corresponds with a human interpretation of textual variance.The article describes research into the automatic comparison of texts with revisions. The authors argue that in-text variation can best be modelled as nonlinear text, and that a collation tool needs to treat in-text variation differently from the way linear text is treated. They describe in detail how the modelling choices they made influence the development of HyperCollate, a collation software that is able to process TEI-XML transcriptions of texts with variation. Consequently, HyperCollate produces a more refined collation output that corresponds with a human interpretation of textual variance.

Publication year:2022

Keywords:A1 Journal article

Accessibility:Open