Task-based Quantitative Evaluation of the Concordance Mosaic Visualization

Researchers working in areas such as lexicography, translation studies, and computational linguistics, use a combination of automated and semi-automated tools to analyze the content of text corpora. Concordancing - or the arranging of passages of a textual corpus in alphabetical order according to user-defined keywords - is one of the oldest and still most widely used forms of text analysis. Concordance Mosaic is an interactive concordance visualization which emphasises quantitative information such as word frequency. While Concordance Mosaic is in active use by humanities scholars, no quantitative evaluation of the technique exists. In this paper, the Concordance Mosaic is quantitatively evaluated in comparison to a typical concordance browser. The comparison is evaluated using speed and accuracy on identified corpus analysis actions.

[1]  Mona Baker,et al.  Epistemologies of evidence-based medicine: a plea for corpus-based conceptual research in the medical humanities , 2021, Medicine, Health Care and Philosophy.

[2]  Shane Sheehan,et al.  Methods and visualization tools for the analysis of medical, political and scientific concepts in Genealogies of Knowledge , 2020, Palgrave Communications.

[3]  J. Buts.  Community and authority in ROAR Magazine , 2020, Palgrave Communications.

[4]  Mona Baker.  Rehumanizing the migrant: the translated past as a resource for refashioning the contemporary discourse of the (radical) left , 2020, Palgrave Communications.

[5]  S. Bernardini,et al.  Corpora , 2019, Routledge Encyclopedia of Translation Studies.

[6]  Steven Franconeri,et al.  Taking Word Clouds Apart: An Empirical Investigation of the Design Space for Keyword Summaries , 2018, IEEE Transactions on Visualization and Computer Graphics.

[7]  Tobias Isenberg,et al.  Visualization as Seen through its Research Paper Keywords , 2014, IEEE Transactions on Visualization and Computer Graphics.

[8]  Yue Wang,et al.  Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two-mode affiliation network , 2016 .

[9]  Gerik Scheuermann,et al.  On the Visualization of Hierarchical Relations and Tree Structures with TagSpheres , 2016, VISIGRAPP.

[10]  Saturnino Luz,et al.  A graph based abstraction of textual concordances and two renderings for their interactive visualisation , 2014, AVI.

[11]  Jinho Choi,et al.  Patent keyword network analysis for improving technology development efficiency , 2014 .

[12]  Verena Lyding,et al.  Double Tree: An Advanced KWIC Visualization for Expert Users , 2010, 2010 14th International Conference Information Visualisation.

[13]  P. Krogh,et al.  Corpus , 2018, Encyclopedia of Database Systems.

[14]  Verena Lyding,et al.  Corpus Clouds - Facilitating Text Analysis by Means of Visualizations , 2009, LTC.

[15]  Martin Wattenberg,et al.  The Word Tree, an Interactive Visual Concordance , 2008, IEEE Transactions on Visualization and Computer Graphics.

[16]  Martin Wattenberg,et al.  TIMELINESTag clouds and the case for vernacular visualization , 2008, INTR.

[17]  John Sinclair,et al.  Reading concordances : an introduction , 2003 .

[18]  Mona Baker,et al.  'Corpus Linguistics and Translation Studies: Implications and Applications' , 1993 .

[19]  Jan Svartvik,et al.  Directions in corpus linguistics : proceedings of Nobel Symposium 82, Stockholm, 4-8 August 1991 , 1992 .

[20]  John Sinclair,et al.  Corpus, Concordance, Collocation , 1991 .

[21]  E. Tufte.  Envisioning Information , 1990 .

[22]  H. P. Luhn.  Key word‐in‐context index for technical literature (kwic index) , 1960 .