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1 Total Environment for Text Data Mining Wataru Sunayama Yasufumi Takama Danushka Bollegala Yoko Nishihara Hidekazu Tokunaga Muneo Kushima Mitsunori Matsushita Graduate School of Information Sciences, Hiroshima City University Faculty of System Design, Tokyo Metropolitan University Graduate School of Information Science and Technology, The University of Tokyo Graduate School of Engineering, The University of Tokyo Kagawa National College of Technology Medical Informatics, University of Miyazaki Hospital Faculty of Informatics, Kansai University keywords: text data mining, total environment, data visualization, graphical user interface Summary In this challenge, we develop and distribute an integrated environment to flexibly combine multiple text mining techniques. Text mining techniques include numerous tasks such as salient sentence extraction, keyword extraction, topic extraction, textual coherence evaluation, multi-document summarization, and text clustering. Although tools that individually perform one or more of the above-mentioned tasks exist, it is difficult to integrate and activate multiple tools for a particular task. We attempt to provide the flexibility to integrate numerous tools that exist in the community in our proposed text mining environment. Users can use a customized version of the proposed text mining environment for their specific tasks, thereby concentrating solely on their creative work. 1. (TETDM )
2 SP-A TETDM 3 4 TETDM TETDM TETDM Web ( 1) TETDM 2 1 TETDM TETDM SNS a) b) c) d) e) f) g)
3 Total Environment for Text Data Mining h) a) b) 3 a) b) c) a) b) c) pixel 900pixel pixel pixel ( 4) 2
4 SP-A 2011 TETDM 2 3 TETDM TETDM c) 2 a) b) e) Java Windows, Mac, Linux OS 3 d) b) c) 4 Web CGI h) a) b) f) g) a) b) 6 a) b) d) c) TETDM
5 Total Environment for Text Data Mining ( ) ) 2) 3) 4) [ 07] 5) [ 08a] 6) [ 10] 7) [ 09] 8)2 9) [Newman 04] 10) [ 08b] 1
6 SP-A ) 5) 9) 2) pixel 3) 4) 5) 10) 1) 8) 10) 6) 3 4 TETDM 4. TETDM TETDM 4 1 [Fayyad 96] R[R- Project] R R TETDM Weka[Weka] orange[orange] Weka TETDM DIAMining Text Mining Studio TRUE TELLER Text Mining for Clementine [DIAMining, Mining Studio, TELLER, Clementine] TETDM 4 2
![TETDM TETDM 4 1 [Fayyad 96] R[R- Project] R R TETDM Weka[Weka]](/docs-images/42/20762848/images/page_6.jpg "orange[orange] Weka TETDM DIAMining Text Mining Studio TRUE TELLER Text")
7 Total Environment for Text Data Mining 489 VidaMine[Kimani 03] [] GATE [GATE] LanguageWare [Language] UIMA Unstructured Information Management Architecture [Ferrucci 04] LanguageWare PC Heart of Gold [Heart] XML U-Compare[ 08] UIMA U-Compare 5 UIMA UIMA UIMA 4 3 TETDM (1) (2) (3) 5 ( 5 Legitimated Peripheral
![[Ferrucci 04] LanguageWare PC Heart of Gold [Heart] XML U-Compare[ 08] UIMA](/docs-images/42/20762848/images/page_7.jpg "U-Compare 5 UIMA UIMA UIMA 4 3 TETDM (1) (2) (3) 5 ( 5 Legitimated Peripheral")
8 SP-A 2011 Participation) [Lave 91] TETDM (3) TETDM (2) 4 1 WIKI [ 01] Wiki [Wiki] 2 / MACD [MACD, 99] Yahoo! API[yahoo] API (Application Program Interface) WEB 3 Web TREC [TREC] InfoVis Contest [Plaisant 07] 4 [ 09] 4 4 Community of Practice CoP)[Lave 91] Community of Interests CoI)[Arias 00] TETDM CoP CoI Community of Practice) (Community of Interests) CoP CoI TETDM CoP CoI
![API[yahoo] API (Application Program Interface) WEB 3 Web TREC [TREC] InfoVis Contest [Plaisant 07]](/docs-images/42/20762848/images/page_8.jpg "4 [ 09] 4 4 Community of Practice CoP)[Lave 91] Community of Interests CoI)[Arias 00] TETDM 4 3 1 4")
9 Total Environment for Text Data Mining TETDM 5 1 Web [twitter] 5 2 overview detail TETDM 5 3 [Kushima 10] 5 4 [Daume 06] Wall Street Journal twitter
![TETDM 5 1 Web [twitter] 5 2 overview](/docs-images/42/20762848/images/page_9.jpg "detail TETDM 5 3 [Kushima 10] 5 4")
10 SP-A 2011 (domain adaptation) X 2 ( ) X A B 6. TETDM TETDM [Arias 00] E. Arias, H. Eden, G. Fischer, A. Gorman, and E. Scharff: Transcending the Individual Human Mind: Creating Shared Understanding through Collaborative Design, ACM Trans. on Computer- Human Interaction, Vol.7 No.1, pp , (2000). [Clementine] Text Mining for Clementine ta/ [Daume 06] Hal Daume III and Daniel Marcu: Domain Adaptation for Statistical Classifiers, Journal of Machine Learning Research, Vol 26, pp , (2006). [DIAMining] DIAMining [Fayyad 96] Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth: Knowledge Discovery and Data Mining: Towards a Unifying Framework, KDD, pp.82 88, (1996). [GATE] GATE [] [ 01], Vol.16. No.6, p.893, (2001). [Heart] Heart of Gold [ 08] UIMA U-Compare, Vol.2008, No.67, pp , (2008). [Kimani 03] S. Kimani, S. Lodi, T. Catarci, G. Santucci and C. Sartori: VidaMine:A Visual Data Mining Environment, Journal of Visual Languages and Computing, Vol.15, No.1, pp.37 67, (2004). [Kushima 10] M. Kushima, K. Araki, M. Suzuki, S. Araki, and T. Nikama: Graphic Visualization of the Co-occurrence Analysis Network of Lung Cancer in-patient nursing record, proc. of The International Conference on Information Science and Applications(ICISA 2010), pp , (2010). [Language] LanguageWare jstart/languageware [Lave 91] J. Lave and E. Wenger: Situated Learning: Legitimate Peripheral Participation, Cambridge Univ. Press, (1991). [MACD] MACD ( [ 99] : MACD, (1999). [ 09],, Vol.24, No.2, pp , (2009). [Mining Studio] Text Mining Studio [Newman 04] Newman, M.E.J.: Fast Algorithm for Detecting Community Structure in Networks, Physical Review E 69, , pp. 1 5, (2004). [ 09], Vol.24, No.6, pp , (2009). [orange] orange ( [Plaisant 07] C. Plaisant, J. D. Fekete, and G. Grinstein: Promoting Insight-Based Evaluation of Visualizations: From Contest to Benchmark Repository, IEEE Trans. on Visualization and Computer Graphics, Vol. 14, No.1, pp , (2008). [R-Project] R-Project [ 07],, Vol.J90-D, No.2, pp , (2007). [ 08a], 22, 1B1-1, (2008). [ 08b] Vol.23, No.6, pp , (2008). [ 10], Vol.J93-D, No.10, pp , (2010). [TELLER] TRUE TELLER [TREC] TREC ( [twitter] twitter [Ferrucci 04] Ferrucci, D. and Lally, A. : UIMA: an architectural approach to unstructured information processing in the corporate research environment, Natural Language Engineering, Vol.10, No.3-4, pp , (2004). [Weka] Weka [Wiki] Wiki( [yahoo] Yahoo! API (
![[Clementine] Text Mining for Clementine http://www.spss.co.](/docs-images/42/20762848/images/page_10.jpg "jp/software/modeler ta/ [Daume 06] Hal Daume III and Daniel Marcu: Domain Adaptation for Statistical Classifiers, Journal of Machine Learning Research, Vol 26, pp.101 126, (2006).")
11 Total Environment for Text Data Mining ( ) IEEE, , , 2005 ( ) Web Intelligence ( ) IEEE Danushka Bollegala ( ) Web, ( ) MOS ( ) ACM ( )
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1. Twitter 1 2 3 3 3 Twitter Twitter ( ) Twitter (trendspotter) Twitter 5277 24 trendspotter TRENDSPOTTER DETECTION SYSTEM FOR TWITTER Wataru Shirakihara, 1 Tetsuya Oishi, 2 Ryuzo Hasegawa, 3 Hiroshi Hujita
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