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Constructive visual analytics for text similarity detection

Research output: Contribution to journalArticle

Alfie Abdul-Rahman, Glenn Roe, Mark Olsen, Clovis Gladstone, Richard Whaling, Nicholas Cronk, Min Chen

Original languageEnglish
Pages (from-to)237-248
JournalCOMPUTER GRAPHICS FORUM
Volume36
Issue number1
Early online date19 Feb 2016
DOIs
Publication statusPublished - Jan 2017

King's Authors

Abstract

Detecting similarity between texts is a frequently encountered text mining task. Because the measurement of similarity is typically composed of a number of metrics, and some measures are sensitive to subjective interpretation, a generic detector obtained using machine learning often has difficulties balancing the roles of different metrics according to the semantic context exhibited in a specific collection of texts. In order to facilitate human interaction in a visual analytics process for text similarity detection, we first map the problem of pairwise sequence comparison to that of image processing, allowing patterns of similarity to be visualized as a 2D pixelmap. We then devise a visual interface to enable users to construct and experiment with different detectors using primitive metrics, in a way similar to constructing an image processing pipeline. We deployed this new approach for the identification of commonplaces in 18th-century literary and print culture. Domain experts were then able to make use of the prototype system to derive new scholarly discoveries and generate new hypotheses.

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