This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Conceptual Recurrence Plots: Revealing Patterns in Human Discourse
June 2012 (vol. 18 no. 6)
pp. 988-997
ASCII Text
x 
 
A. Smith, D. Angus, J. Wiles, "Conceptual Recurrence Plots: Revealing Patterns in Human Discourse," IEEE Transactions on Visualization and Computer Graphics, vol. 18, no. 6, pp. 988-997, June, 2012.
 
 
BibTex
x
 
@article{ 10.1109/TVCG.2011.100,
author = {A. Smith and D. Angus and J. Wiles},
title = {Conceptual Recurrence Plots: Revealing Patterns in Human Discourse},
journal ={IEEE Transactions on Visualization and Computer Graphics},
volume = {18},
number = {6},
issn = {1077-2626},
year = {2012},
pages = {988-997},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2011.100},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - Conceptual Recurrence Plots: Revealing Patterns in Human Discourse
IS - 6
SN - 1077-2626
SP988
EP997
EPD - 988-997
A1 - A. Smith,
A1 - D. Angus,
A1 - J. Wiles,
PY - 2012
KW - time series
KW - data visualisation
KW - text analysis
KW - conversation transcripts
KW - conceptual recurrence plot technique
KW - pattern revealing
KW - human discourse
KW - conceptual information
KW - global-local pattern visualization
KW - data stream
KW - information visualization technique
KW - complex time series data
KW - point similarity measurement
KW - term-based similarity
KW - language modelling
KW - text utterances
KW - interaction pattern discovery
KW - Data visualization
KW - Semantics
KW - Humans
KW - Image color analysis
KW - Pain
KW - Surgery
KW - Time series analysis
KW - text analysis.
KW - Concept map
KW - recurrence
KW - concept
KW - plotting
KW - conversation analysis
VL - 18
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
A. Smith, Sch. of Inf. Technol. & Electr. Eng., Univ. of Queensland, Brisbane, QLD, Australia
D. Angus, Sch. of Inf. Technol. & Electr. Eng., Univ. of Queensland, Brisbane, QLD, Australia
J. Wiles, Sch. of Inf. Technol. & Electr. Eng., Univ. of Queensland, Brisbane, QLD, Australia
Human discourse contains a rich mixture of conceptual information. Visualization of the global and local patterns within this data stream is a complex and challenging problem. Recurrence plots are an information visualization technique that can reveal trends and features in complex time series data. The recurrence plot technique works by measuring the similarity of points in a time series to all other points in the same time series and plotting the results in two dimensions. Previous studies have applied recurrence plotting techniques to textual data; however, these approaches plot recurrence using term-based similarity rather than conceptual similarity of the text. We introduce conceptual recurrence plots, which use a model of language to measure similarity between pairs of text utterances, and the similarity of all utterances is measured and displayed. In this paper, we explore how the descriptive power of the recurrence plotting technique can be used to discover patterns of interaction across a series of conversation transcripts. The results suggest that the conceptual recurrence plotting technique is a useful tool for exploring the structure of human discourse.

[1] R. Amar and J. Stasko, “A Knowledge Task-Based Framework for Design and Evaluation of Information Visualizations,” Proc. IEEE Symp. Information Visualization (InfoVis '04), 2004.
[2] D.M. Blei et al., “Latent Dirichlet Allocation,” J. Machine Learning Research, vol. 3, pp. 993-1022, 2003.
[3] K.W. Church and J.I. Helfman, “Dotplot: A Program for Exploring Self-Similarity in Millions of Lines of Text and Code,” J. Computational and Graphical Statistic, vol. 2, no. 2, pp. 153-174, 1993.
[4] R. Dale and M.J. Spivey, “Unraveling the Dyad: Using Recurrence Analysis to Explore Patterns of Syntactic Coordination between Children and Caregivers in Conversation,” Language Learning, vol. 56, no. 3, pp. 391-430, 2006.
[5] J.-P. Eckmann et al., “Recurrence Plots of Dynamical Systems,” Europhysics Letters, vol. 5, pp. 973-977, 1987.
[6] J. Foote, “Visualizing Music and Audio Using Self-Similarity,” Proc. Seventh ACM Int'l Conf. Multimedia, pp. 77-80, 1999.
[7] S. Havre et al., “Themeriver: Visualizing Theme Changes over Time,” Proc. IEEE Symp. Information Visualization (InfoVis '00), pp. 115-123, 2000.
[8] J.I. Helfman, “Similarity Patterns in Language,” Proc. IEEE Symp. Visual Languages, pp. 173-175, Oct. 1994.
[9] V.K. Jacek Wolkowicz and S. Brooks, “Midivis: Visualizing Music Pieces Structure via Similarity Matrices,” Proc. Int'l Computer Music Conf. (ICMC '09), 2009.
[10] D. Keim et al., “Recursive Pattern: A Technique for Visualizing Very Large Amounts of Data,” Proc. IEEE Conf. Visualization (Visualization '95), pp. 279-286, 1995.
[11] T. Landauer et al., “Introduction to Latent Semantic Analysis,” Discourse Processes, vol. 25, pp. 259-284, 1998.
[12] F. Orsucci et al., “Orthographic Structuring of Human Speech and Texts: Linguistic Application of Recurrence Quantification Analysis,” Int'l J. Chaos Theory and Applications, vol. 4, nos. 2/3, pp. 21-28, 1999.
[13] G. Salton, Automatic Text Processing : The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, 1989.
[14] B. Shneiderman, “Inventing Discovery Tools: Combining Information Visualization with Data Mining,” Information Visualization, vol. 1, no. 1, pp. 5-12, 2002.
[15] A.E. Smith and M.S. Humphreys, “Evaluation of Unsupervised Semantic Mapping of Natural Language with Leximancer Concept Mapping,” Behavior Research Methods, vol. 38, no. 2, pp. 262-279, 2006.
[16] M. Wattenberg, “Arc Diagrams: Visualizing Structure in Strings,” Proc. IEEE Symp. Information Visualization (InfoVis '02), pp. 110-116, 2002.
[17] C.L. Webber and J.P. Zbilut, “Dynamical Assessment of Physiological Systems and States Using Recurrence Plot Strategies,” J. Applied Physiology, vol. 76, no. 2, pp. 965-973, 1994.
[18] S. Yates et al., Discourse as Data: A Guide for Analysis. Sage Publications, 2001.

Index Terms:
time series,data visualisation,text analysis,conversation transcripts,conceptual recurrence plot technique,pattern revealing,human discourse,conceptual information,global-local pattern visualization,data stream,information visualization technique,complex time series data,point similarity measurement,term-based similarity,language modelling,text utterances,interaction pattern discovery,Data visualization,Semantics,Humans,Image color analysis,Pain,Surgery,Time series analysis,text analysis.,Concept map,recurrence,concept,plotting,conversation analysis
Citation:
A. Smith, D. Angus, J. Wiles, "Conceptual Recurrence Plots: Revealing Patterns in Human Discourse," IEEE Transactions on Visualization and Computer Graphics, vol. 18, no. 6, pp. 988-997, June 2012, doi:10.1109/TVCG.2011.100
Usage of this product signifies your acceptance of the Terms of Use.