Constructive Visual Analytics for Text Similarity Detection

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dc.contributor.authorAbdul-Rahman, A.en_US
dc.contributor.authorRoe, G.en_US
dc.contributor.authorOlsen, M.en_US
dc.contributor.authorGladstone, C.en_US
dc.contributor.authorWhaling, R.en_US
dc.contributor.authorCronk, N.en_US
dc.contributor.authorMorrissey, R.en_US
dc.contributor.authorChen, M.en_US
dc.contributor.editorChen, Min and Zhang, Hao (Richard)en_US
dc.date.accessioned2017-03-13T18:13:03Z
dc.date.available2017-03-13T18:13:03Z
dc.date.issued2017
dc.description.abstractDetecting 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.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.en_US
dc.description.number1
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume36
dc.identifier.doi10.1111/cgf.12798
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.12798
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf12798
dc.publisher© 2017 The Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectVisualization
dc.subjectVisual Analytics
dc.subjectI.3.6 [Computer Graphics]: Methodology and Techniques—Interaction techniques I.4.m [Image Processing and Computer Vision]: Miscellaneous I.7.5 [Document and Text Processing]: Document Capture—Document Analysis
dc.titleConstructive Visual Analytics for Text Similarity Detectionen_US
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