Indices of novelty for emerging topic detection

Information Processing and Management

Volumn 48, Issue 2, 2012, Pages 303-325

  Tu, Yi Ning ^a   Seng, Jia Lang ^b  

^a FU JEN CATHOLIC UNIVERSITY   (Taiwan)

^b NATIONAL CHENGCHI UNIVERSITY   (Taiwan)

Author keywords

Aging theory; Information retrieval; Novelty index; Published volume index; Text mining; Topic detection and tracking

Indexed keywords

DETECTION POINT; EMERGING TOPICS; LIFE SPAN; NOVELTY INDICES; RESEARCH AREAS; RESEARCH TRENDS; TEXT MINING; TOPIC DETECTION AND TRACKING; VOLUME INDEX;

DATA MINING; DIGITAL LIBRARIES; INFORMATION RETRIEVAL;

RESEARCH;

EID: 84857373275     PISSN: 03064573     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ipm.2011.07.006     Document Type: Article

References (56)

1. Allan, J., Carbonell, J., Doddington, G., Yamron, J., & Yang, T. (1998). Topic detection and tracking pilot study: Final report. In Proceedings of the DARPA broadcast news transcription an understanding workshop.
2. Allan, J., Papka, R., & Lavrenko, V. (1998). On-line new event detection and tracking. In Proceedings of the 21st annual international ACM SIGIR conference on research and development in information retrieval (pp. 37-45).
3. P.P. Aurora, B.L. Rafael, and R.S. Jose Topic discovery based on text mining techniques Information Processing & Management 43 2007 742 768
4. M.W. Berry Survey of text mining-clustering, classification, and retrieval 2004 Springer pp. 185-224
5. T. Braun, A.P. Schubert, and R.N. Kostoff Grwth and trends of fullerence research as reflected in its journal literature Chemical Reviews 100 1 2000 23 27
6. Chen, C. C., Chen, Y. T., Sun, Y., & Chen, M. C. (2003). Life cycle modeling of news events using aging theory. In Proceeding of 14th European conference machine learning (ECML '03) (pp. 47-59). (Pubitemid 37230965)
7. K.Y. Chen, L. Luesukprasert, and S.C. Chou Hot topic extraction based on timeline analysis and multidimensional sentence modeling IEEE Transactions on knowledge and data enginerting 19 8 2007 1016 1025 (Pubitemid 47000340)
8. T.C. Chou, and M.C. Chen Using incremental PLSI for threshold-resilient online event analysis IEEE Transactions on Knowledge and Data Engineering 20 3 2008 289 299 (Pubitemid 351185381)
9. C. Clifton, R. Cooley, and J. Rennie Topcat: Data mining for topic identification in a text corpus IEEE Transactions on Knowledge and Data Engineering 16 8 2004 949 964
10. Cui, C., & Kitagawa, H. (2005). Topic activation analysis for document streams based on document arrival rate and relevance. In Proceedings of the 2005 ACM symposium on applied computing (pp. 1089-1095).
11. S.W. Cunningham, A.L. Porter, and N.C. Newman Introduction - Special issue on tech mining Technological Forecasting & Social Change 73 2006 915 922 (Pubitemid 44296266)
12. T.U. Daim, G. Rueda, H. Martin, and P. Gerdsri Forecasting emerging technologies: Use of bibliometrics and patent analysis Technological Forecasting & Social Change 73 2006 981 1012 (Pubitemid 44296265)
13. Franz, M., & McCarley, J. C. (2001). Unsupervised and supervised clustering for topic tracking. In Proceedings of the 24th annual international ACM SIGIR conference on research and development in information retrieval (pp. 310-317). (Pubitemid 32951085)
14. Hatzivassiloglou, V., Gravano, L., & Maganti, A. (2000). An investigation of linguistic features and clustering algorithms. In Proceedings of the 23rd annual international ACM SIGIR conference on research and development in information retrieval (pp. 224-231).
15. Y. Jin, S.H. Myaeng, and Y. Jung Use of place information for improved event tracking Information Processing & Management 43 2007 365 378 (Pubitemid 44646715)
16. Jo, Y., Lagoze, C., & Giles, C. L. (2007). Detecting research topics via the correlation between graphs and texts. In Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 370-379). (Pubitemid 350229224)
17. Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the EMNLP conference.
18. Kleinberg, J. (2002). Bursty and hierarchical structure in streams. In Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 91-101).
19. G. Kollios, D. Gunopulos, N. Koudas, and S. Berchtold Efficient biased sampling for approximate clustering and outlier detection in large data sets IEEE Transactions on Knowledge and Data Engineering 15 5 2003 1170 1187
20. R.N. Kostoff Literature-related discovery (LRD): Introduction and background Technological Forecasting and Social Change 75 2008 165 185 (Pubitemid 351168324)
21. R.N. Kostoff, S. Bhattacharya, and M. Pecht Assessment of China's and India's science and technology literature-introduction, background, and approach Technological Forecasting and Social Change 74 2007 1538 1591
22. R.N. Kostoff, M.B. Briggs, R.L. Rushenberg, C.A. Bowles, A.S. Icenhour, and K.F. Nikodym Chinese science and technology - Structure and infrastructure Technological Forecasting and Social Change 74 2007 1539 1573 (Pubitemid 350111973)
23. R.N. Kostoff, M.B. Briggs, R.L. Rushenberg, C.A. Bowles, M. Pecht, and D. Johnson Comparisons of the structure and infrastructure of Chinese and Indian science and technology Technological Forecasting and Social Change 74 2007 1609 1630 (Pubitemid 350123388)
24. R.N. Kostoff, M.B. Briggs, J.L. Solka, and R.L. Rushenberg Literature-related discovery (LRD): Methodology Technological Forecasting and Social Change 75 2008 186 202 (Pubitemid 351168325)
25. R.N. Kostoff, D. Johnson, C.A. Bowles, S. Bhattacharya, A.S. Icenhour, and K. Nikodym Assessment of India's research literature Technological Forecasting and Social Change 74 2007 1574 1608 (Pubitemid 350123387)
26. M. Kuramochi, and G. Karypis An efficient algorithm for discovering frequent subgraphs IEEE Transactions on Knowledge and Data Engineering 16 9 2004 1038 1051
27. Lee, C., Lee, G. G., Jang, M. (2007). Dependency structure language model for topic detection and tracking. Information Processing & Management, 43, 1249-1259. (Pubitemid 46656430)
28. Z. Lee, S. Gosain, and I. Im Topics of interest in IS: Evolution of themes and differences between research and practice Information & Management 36 1997 233 246 (Pubitemid 129592091)
29. J. Malone, K. McGarry, and C. Bowerman Automated trend analysis of proteomics data using an intelligent data mining architecture Expert Systems with Applications 30 2006 24 33 (Pubitemid 41641816)
30. J. Makkonen, H. Ahonen-Myka, and M. Salmenkivi Simple semantics in topic detection and tracking Information Retrieval 7 3-4 2004 347 368 (Pubitemid 39046502)
31. Manmatha, R., Feng, A., Allan, J. (2002). A critical examination of TDT's cost function. In Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval (pp. 403-404).
32. Morinaga, S., & Yamanishi, K. (2004). Tracking dynamics of topic trends using a finite mixture model. In Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 811-816). (Pubitemid 40115012)
33. Nallapati, R., Ahmed, A., Xing, E. P., & Cohen, W. W. (2008). Joint latent topic models for text and citations. In Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 542-550).
34. J. Ontrup, H. Ritter, S.W. Scholz, and R. Wagner Detecting, assessing and monitoring relevant topics in virtual information environments IEEE Transactions on Knowledge and Data Engineering 20 7 2008
35. S. Ozmutlu Automatic new topic identification using multiple linear regression Information Processing & Management 42 2006 934 950 (Pubitemid 41828294)
36. H.C. Ozmutlu, and F. Cavdur Application of automatic topic identification on excite web search engine data logs Information Processing & Management 41 2005 1243 1262 (Pubitemid 40455271)
37. A.L. Porter, and S.W. Cunningham Tech mining: Exploiting new technologies for competitive advantage 2005 Wiley-Interscience New York
38. G. Salton, A. Wong, and C.S. Yang A vector space model fro automatic indexing Communications of the ACM 18 11 1975 613 620
39. Schultz, J. M., & Liberman, M. (1999). Topic detection and tracking using idf-weighted cosine coefficient. In Proceedings of the DARPA broadcast news transcription an understanding workshop.
40. Shibata, N., Kajikawa, Y., Takeda, Y., Sakata, I., & Matsushima, K. (2010). Detection emerging research fronts in regenerative medicine by the citation network analysis of scientific publications. Technological Forecasting & Social Change.
41. N. Shibata, Y. Kajikawa, and K. Matsushima Topological analysis of citation networks to discover the future core articles Journal of the American Society for Information Science and Technology 58 6 2007 872 882 (Pubitemid 46591307)
42. N. Shibata, Y. Kajikawa, Y. Takeda, and K. Matsushima Detection emerging research fronts based on topological measures in citation networks of scientific publications Technovation 28 2008 758 775
43. th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 306-315). (Pubitemid 40114940)
44. Stokes, N., & Carthy, J. (2001). Combining semantic and syntactic document classifiers to improve first story detection. In Proceedings of the 24th annual international ACM SIGIR conference on research and development in information retrieval (pp. 424-425). (Pubitemid 32951113)
45. Swan, R., & Allan, J. (2000). Automatic generation of overview timelines. In Proceedings of the 23rd annual international ACM SIGIR conference on research and development in information retrieval (pp. 49-56).
46. T.A. Tran, and T. Daim A taxonomic review of methods and tools applied in technology assessment Technological Forecasting & Social Change 75 2008 1396 1405
47. Y.N. Tu, and J.L. Seng Research intelligence involving information retrieval-An example of conferences and journals Expert Systems with Applications 36 10 2009 12151 12166
48. Walls, F., Jin, H., Sista, S., & Schwartz, R. (1999). Topic detection in broadcast news. In Proceedings of the DARPA broadcast news transcription an understanding workshop.
49. Wang, X., Zhai, C., Hu, X., & Sproat, R. (2007). Mining correlated bursty topic patterns from coordinated text streams. In Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 784-793). (Pubitemid 350229264)
50. K. Wu, M. Chen, and Y. Sun Automatic topics discovery from hyperlinked documents Information Processing & Management 40 2004 239 255
51. Yang, Y., Ault, T., Pierce T., & Lattimer, C. W. (2000). Improving text categorization methods for event tracking. In Proceedings of the 23th annual international ACM SIGIR conference on research and development in information retrieval (pp. 65-72).
52. Yang, Y., Zhang, J., Carbonell, J., & Jin, Chun. (2002). Topic-conditioned novelty detection. In Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 688-693).
53. Yang, Y., Yoo, S., Zhang, J., & Kisiel, B. (2005). Robustness of adaptive filtering methods in a cross-benchmark evaluation. In Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval (pp. 98-105).
54. Zhang, Y., Callan, J., & Minka, T. (2002). Novelty and redundancy detection in adaptive filtering. In Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval (pp. 81-88).
55. Zhang, Y., Surendran, A. C., Platt, J. C., & Narasimhan, M. (2008). Learning from multi-topic web documents for contextual advertisement. In Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1051-1059).
56. D. Zhu, and A.L. Porter Automated extraction and visualization of information for technological intelligence and forecasting Technological Forecasting & Social Change 69 2002 495 506 (Pubitemid 34758009)