Efficient linear text segmentation based on information retrieval techniques

Proceedings of the International Conference on Management of Emergent Digital EcoSystems, MEDES '09

Volumn , Issue , 2009, Pages 167-171

  Kern, Roman ^a   Granitzer, Michael ^b  

^a KNOW CENTER   (Austria)

^b GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Sentence similarity; Sliding window; Text segmentation

Indexed keywords

BENCHMARK DATA; SENTENCE SIMILARITY; SLIDING WINDOW; TEXT DOCUMENT; TEXT SEGMENTATION;

COMPUTATIONAL COMPLEXITY; COMPUTATIONAL EFFICIENCY; INFORMATION SERVICES; WINDOWS;

INFORMATION RETRIEVAL;

EID: 74549147972     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1643823.1643854     Document Type: Conference Paper

References (19)

1. D. Beeferman, A. Berger, and J. Lafferty. Statistical models for text segmentation. In Machine Learning, pages 177-210, 1999.
2. B. Boguraev, M. Neff, I. Center, and N. Yorktown Heights. Discourse segmentation in aid of document summarization. In System Sciences, 2000. Proceedings of the 33rd Annual Hawaii International Conference on, page 10, 2000.
3. T. Brants, F. Chen, and I. Tsochantaridis. Topic-based document segmentation with probabilistic latent semantic analysis. In Proceedings of the eleventh international conference on Information and knowledge management, pages 211{218. ACM New York, NY, USA, 2002.
4. F. Choi, P. Wiemer-Hastings, and J. Moore. Latent semantic analysis for text segmentation. In In Proceedings of EMNLP, 2001.
5. F. Y. Y. Choi. Advances in domain independent linear text segmentation, 2000.
6. G. Dias and E. Alves. Unsupervised topic segmentation based on word co-occurrence and multi-word units for text summarization. In Proceedings of the ELECTRA Workshop associated to 28th ACM SIGIR Conference, Salvador, Brazil, pages 41-48, 2005.
7. S. T. Gries. Dispersions and adjusted frequencies in corpora. International Journal of Corpus Linguistics, 13(4):403-437.
8. M. Hearst. TextTiling: Segmenting text into multi-paragraph subtopic passages. Computational Linguistics, 23(1):33-64, 1997.
9. M. A. Hearst. Multi-paragraph segmentation of expository text, 1994.
10. O. Heinonen. Optimal multi-paragraph text segmentation by dynamic programming. In ANNUAL MEETING-ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, volume 36, pages 1484-1486. ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, 1998.
11. X. Ji and H. Zha. Domain-independent text segmentation using anisotropic diffusion and dynamic programming. In In Proceedings of SIGIR, pages 322-329. ACM Press, 2003.
12. A. Kehagias, F. Pavlina, and V. Petridis. Linear text segmentation using a dynamic programming algorithm. In Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics, April, pages 12-17, 2003.
13. Y. Li, D. McLean, Z. Bandar, J. O'Shea, and K. Crockett. Sentence similarity based on semantic nets and corpus statistics. IEEE Transactions on Knowledge and Data Engineering, pages 1138-1150, 2006.
14. L. Pevzner and M. A. Hearst. A critique and improvement of an evaluation metric for text segmentation. Computational Linguistics, 28:1-19, 2002.
15. J. Reynar. Statistical models for topic segmentation. In Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics, pages 357-364. Association for Computational Linguistics Morristown, NJ, USA, 1999.
16. K. Richmond and A. Smith. Detecting subject boundaries within text: A language independent statistical approach. In Brown University, Providence, Rhode Island, pages 47-54, 1997.
17. C. Sporleder and M. Lapata. Broad coverage paragraph segmentation across languages and domains. ACM Trans. Speech Lang. Process, 3:1-35, 2006.
18. B. Sun, P. Mitra, C. Giles, J. Yen, and H. Zha. Topic segmentation with shared topic detection and alignment of multiple documents. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 199-206. ACM New York, NY, USA, 2007.
19. M. Utiyama and H. Isahara. A statistical model for domain-independent text segmentation. In ANNUAL MEETING-ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, volume 39, pages 491-498, 2001.