"Term clumping" for technical intelligence: A case study on dye-sensitized solar cells

Technological Forecasting and Social Change

Volumn 85, Issue , 2014, Pages 26-39

  Zhang, Yi ^a   Porter, Alan L ^b,c   Hu, Zhengyin ^d   Guo, Ying ^a   Newman, Nils C ^e  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^c Search Technology Inc   (United States)

^d CHINESE ACADEMY OF SCIENCES   (China)

^e Intelligent Information Systems Corporation (IISC)   (United States)

Author keywords

DSSCs; Dye sensitized solar cells; Tech mining; Technical intelligence; Term clumping; Text analytics; Text clustering

Indexed keywords

ABSTRACTING; EXTRACTION; PHOTOELECTROCHEMICAL CELLS;

DSSCS; DYE-SENSITIZED SOLAR CELL; TECH MININGS; TECHNICAL INTELLIGENCE; TERM CLUMPING; TEXT ANALYTICS; TEXT CLUSTERING;

SOLAR CELLS;

CLUSTER ANALYSIS; DYE; EMPIRICAL ANALYSIS; INNOVATION; MINING; RESEARCH AND DEVELOPMENT; SOLAR POWER;

EID: 84900551843     PISSN: 00401625     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.techfore.2013.12.019     Document Type: Article

References (47)

1. VantagePoint, www.theVantagePoint.com (accessed 5 August 2013) also see Thomson Data Analyzer (TDA), http://thomsonreuters.com/thomson-data-analyzer/.
2. Porter A.L., Detampel M.J. Technology opportunity analysis. Technol. Forecast. Soc. Change 1995, 49:237-255.
3. Porter A.L., Cunningham S.W. Tech Mining: Exploiting New Technologies for Competitive Advantage 2005, Wiley, New York, NY.
4. Watts R.J., Porter A.L., Cunningham S.W., Zhu D. TOAS intelligence mining, an analysis of NLP and computational linguistics. Lect. Notes Comput. Sci. 1997, 1997(1263):323-334.
5. Zhu D., Porter A.L. Automated extraction and visualization of information for technological intelligence and forecasting. Technol. Forecast. Soc. Change 2002, 69:495-506.
6. Losiewicz P., Oard D.W., Kostoff R.N. Textual data mining to support science and technology management. J. Intell. Inf. Syst. 2002, 15(2):99-119.
7. D. K. R. Robinson, L. Huang, Y. Guo, A. L. Porter, Forecasting innovation pathways for new and emerging science & technologies, Technological Forecasting & Social Change, to appear.
8. Kim Y., Tian Y., Jeong Y., Ryu J., Myaeng S. Automatic discovery of technology trends from patent text. Proceedings of the 2009 ACM symposium on Applied Computing, Hawaii, USA 2009.
9. Verbitsky M. Semantic TRIZ. TRIZ J. Feb 2004, (http://www.triz-journal.com/archives/2004/ (accessed 20 May 2012)).
10. Zhang Y., Guo Y., Wang X., Zhu D., Porter A.L. A hybrid visualization model for technology roadmapping: bibliometrics, qualitative methodology, and empirical study. Tech. Anal. Syst. Manag. 2013, 25(6):707-724.
11. Price D.S. Little Science, Big Science and Beyond 1986, Columbia University Press, New York, NY.
12. Garfield E., Malin M., Small H. Citation data as science indicators. The Metric of Science: the Advent of Science Indicators 1978, Wiley, New York, NY. Y. Elkana (Ed.).
13. van Raan A.F.J. Advanced bibliometric methods to assess research performance and scientific development: basic principles and recent practical applications. Res. Eval. 1992, 3(3):151-166.
14. De Bellis N. Bibliometrics and Citation Analysis 2009, The Scarecrow Press, Lanham, MD.
15. Ma T., Porter A.L., Ready J., Xu C., Gao L., Wang W., Guo Y. A technology opportunities analysis model: applied to dye-sensitized solar cells for China. 4th International Seville Conference on "Future-oriented Technology Analysis (FTA)," Spain 2011.
16. Guo Y., Xu C., Huang L., Porter A.L. Empirically informing a technology delivery system model for an emerging technology: illustrated for dye-sensitized solar cells. R&D Manag. 2012, 42(2):133-149.
17. Bookstein A., Klein T., Raita T. Clumping properties of content-bearing words. J. Am. Soc. Inf. Sci. 1998, 49(2):102-114.
18. Bookstein A., Raita T. Discovering term occurrence structure in text. J. Am. Soc. Inf. Sci. Technol. 2000, 52(6):476-486.
19. Bookstein A., Vladimir K., Raita T., John N. Adapting measures of clumping strength to assess term-term similarity. J. Am. Soc. Inf. Sci. Technol. 2003, 54(7):611-620.
20. Allan J., Carbonell J., Doddington G., Yamron J., Yang Y. Topic detection and tracking pilot study final report. Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop 1998.
21. Nallapati R. Semantic language models for topic detection and tracking. Proceedings of the conference of the North American chapter of the Association for Computational Linguistics on Human Language Technology 2003, 1-6.
22. Cunningham S.W. The content evaluation of British scientific research 1996, D.Phil. Thesis, Science Policy Research Unit, University of Sussex, Brighton, United Kingdom.
23. Haywood, S. Academic Vocabulary, Nottingham University.http://www.nottingham.ac.uk/~alzsh3/acvocab/wordlists.htm (accessed 26 May, 2012).
24. Fodor I.K. A survey of dimension reduction techniques 2002, U.S. Department of Energy, Lawrence Livermore National Lab, (https://e-reports-ext.llnl.gov/pdf/240921.pdf (accessed 22 May 2012)).
25. Deerwester S., Dumais S.T., Furnas G.W., Landauer T.K., Harshman R.A. Indexing by latent semantic analysis. J. Soc. Inf. Sci. 1990, 41(6):391-407.
26. Handbook of Latent Semantic Analysis 2007, Erlbaum Associates, Mahwah, NJ. T.K. Landauer, D.S. McNamara, S. Denis, W. Kintsch (Eds.).
27. Blei D., Ng A., Jordan M. Latent Dirichlet allocation. J. Mach. Learn. Res. 2003, 3:993-1022.
28. Berry M.W., Castellanos M. Survey of text mining II: clustering, classification, and retrieval 2008, Springer, New York, NY.
29. Pottenger W., Yang T. Detecting emerging concepts in textual data mining. Comput. Inf. Retr. 2001, 1-17.
30. Watts R.J., Porter A.L. Mining foreign language information resources. Proceedings of Portland International Conference on Management of Engineering and Technology, Portland, OR, USA 1999.
31. Thomas H. Probabilistic latent semantic indexing. Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in, information retrieval 1999, 50-57.
32. Ando R.K. Latent semantic space: iterative scaling improves precision of inter-document similarity measurement. Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval 2000, 216-223.
33. Ding W., Yu S.N., Yu S.Q., Wei W., Wang Q. LRLW-LSI: an improved latent semantic indexing (LSI) text classifier. Proceedings of the 3rd international conference on Rough sets and knowledge technology 2008, 483-490.
34. Xu L., Furlotte N., Lin Y., Heinrich K., Berry M.W., George E.O., Homayouni R. Functional cohesion of gene sets determined by latent semantic indexing of PubMed abstracts. PLoS One 2011, 6(4):1-9.
35. Maletic J.I., Marcus A. Using latent semantic analysis to identify similarities in source code to support program understanding. 12th IEEE International Conference on Tools with Artificial Intelligence 2001, 46-53.
36. Maletic J.I., Marcus A. Supporting program comprehension using semantic and structural information. Proceedings of the 23rd International Conference on Software Engineering 2001, 103-112.
37. Mei Q., Zhai C. A mixture model for contextual text mining. Proceedings of the 12th ACM SIGKDD international conference on Knowledge Discovery and Data Mining 2006, 649-655.
38. Sayyadi H., Hurst M., Maykov A. Event detection and story tracking in social streams. Proceeding of 3rd Int'l AAAI Conference on Weblogs and Social Media, San Jose, California, USA 2009.
39. Shinmori A., Okumura M., Marukawa Y., Iwayama M. Patent claim processing for readability: structure analysis and term explanation. Proceedings of the ACL-03 workshop on patent corpus processing 2003, 56-65.
40. Zhang Y., Zhou X., Porter A.L., Gomila J.M.V. How to combine term clumping and technology roadmapping for newly emerging science & technology competitive intelligence: the semantic TRIZ tool and case study. 14th International Society of Scientometrics and Informetrics Conference, Vienna, Austria 2013.
41. Text Clumping for Technical Intelligence, Theory and Applications for Advanced Text Mining 2012, Tech Publishing, Croatia. A.L. Porter, Y. Zhang, S. Sakurai (Eds.).
42. Newman N.C., Porter A.L., Newman D., Courseault-Trumbach C., Bolan S.D. Comparing Methods to Extract Technical Content for Technological Intelligence. Portland International Conference on Management of Engineering and Technology 2012, Vancouver, Canada.
43. L. Huang, Y. Guo, T. Ma, A. L. Porter, Text mining of information resources to inform forecasting of innovation pathways, Technology Analysis & Strategic Management, to appear.
44. O'Regan B., Grätzel M. A low-cost, high efficiency solar-cell based on dye-sensitized colloidal TiO2 films. Nature 1991, 353(6346):737-740.
45. Trumbach C.C., Payne D. Identifying synonymous concepts in preparation for technology mining. J. Inf. Sci. 2007, 33(6):660-677.
46. Y. Zhang, X. Zhou, A. L. Porter, J. M. V. Gomila, Triple helix innovation in China's dye-sensitized solar cell industry: hybrid methods with semantic TRIZ and technology roadmapping, scientometrics, DOI: 10.1007/s11192-013-1090-9.
47. The Information Revolution: Current and Future Consequences 1998, JAI/Ablex, Westport, CT. A.L. Porter, W. Read (Eds.).