Constructive visual analytics for text similarity detection

Journal article

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.

Authors: Abdul-Rahman, A; Roe, G; Olsen, M; Gladstone, C; Whaling, R

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Wiley
• Journal: Computer Graphics Forum
• Publication date: 2016-02-19
• Acceptance date: 2015-12-17
• DOI: 10.1111/cgf.12798
• EISSN: 1467-8659
• ISSN: 0167-7055
• Keywords: visual analytics; I.3.6 [Computer Graphics]: Methodology and Techniques—Interaction techniques; similarity detection; I.4.m [Image Processing and Computer Vision]: Miscellaneous; model developmental visualization; model editing interface; commonplace; visualization; I.7.5 [Document and Text Processing]: Document Capture—Document Analysis; text analysis