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Order Estimation of Japanese Paragraphs by Supervised Machine Learning and Various Textual Features Cover

Order Estimation of Japanese Paragraphs by Supervised Machine Learning and Various Textual Features

Open Access
|Oct 2015

References

  1. [1] Rakesh Agrawal and Ramakrishnan Srikant. Mining sequential patterns. Proceedings of ICDEf95, pages 3-14, 1995.
  2. [2] Danushka Bollegala, Naoaki Okazaki, and Mitsuru Ishizuka. A bottom-up approach to sentence ordering for multi-document summarization. Proceedings of the 44th Annual Meeting of the Association of Computational Linguistics, pages 385-392, 2006.10.3115/1220175.1220224
  3. [3] Jaime Carbonell and Jade Goldstein. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 335-336, 1998.10.1145/290941.291025
  4. [4] Nello Cristianini and John Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 2000.10.1017/CBO9780511801389
  5. [5] Fosca Giannotti, Mirco Nanni, and Dino Pedreschi. Efficient mining of temporally annotated sequences. Proceedings of the 2006 SIAM International Conference on Data Mining, pages 348-359, 2006.10.1137/1.9781611972764.31
  6. [6] Yuya Hayashi, Masaki Murata, Liangliang Fan, and Masato Tokuhisa. Japanese sentence order estimation using supervised machine learning with rich linguistic clues. In Proceedings of the 14th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2013), pages 1-12, 2013.
  7. [7] Nikiforos Karamanis and Hisar Maruli Manurung. Stochastic text structuring using the principle of continuity. In Proceedings of the second International Natural Language Generation Conference (INLGf02), pages 81-88, 2002.
  8. [8] Taku Kudoh. TinySVM: Support Vector Machines. http://cl.aist-nara.ac.jp/taku-ku//software/TinySVM/index.html, 2000.
  9. [9] Mirella Lapata. Probablistic text structuring: Experiments with sentence ordering. Proceedings of the 41st Annual Meeting of the Association of Computational Linguistics, pages 542-552, 2003.10.3115/1075096.1075165
  10. [10] William C. Mann and Sandra A. Thompson. Rhetorical structure theory: Toward a functional theory of text organization. Text, 8(3):243-281, 1988.
  11. [11] Yuji Matsumoto, Akira Kitauchi, Tatsuo Yamashita, Yoshitaka Hirano, Hiroshi Matsuda, and Masayuki Asahara. Japanese morphological analysis system ChaSen version 2.0 manual 2nd edition. 1999.
  12. [12] Kathleen R. McKeown, Judith L. Klavans, Vasileios Hatzivassiloglou, Regina Barzilay, and Eleazar Eskin. Towards multidocument summarization by reformulation: Progress and prospects. In Proceedings of AAAI/IAAI, pages 453-460, 1999.
  13. [13] Masaki Murata and Hitoshi Isahara. Automatic detection of mis-spelled Japanese expressions using a new method for automatic extraction of negative examples based on positive examples. IEICE Transactions on Information and Systems, E85- D(9):1416-1424, 2002.
  14. [14] Masaki Murata, Satoshi Ito, Masato Tokuhisa, and Qing Ma. Order estimation of Japanese paragraphs by supervised machine learning. Proceedings of SCIS-ISIS 2014, pages 1096-1101, 2014.
  15. [15] Naoaki Okazaki, Yutaka Matsuo, and Mitsuru Ishizuka. Improving chronological sentence ordering by precedence relation. In Proceedings of the 20th International Conference on Computational Linguistics (COLING 04), pages 750-756, 2004.10.3115/1220355.1220463
  16. [16] Kiyotaka Uchimoto, Masaki Murata, Qing Ma, Satoshi Sekine, and Hitoshi Isahara. Word order acquisition from corpora. In COLING ’2000, pages 871-877, 2000.10.3115/992730.992772
Language: English
Page range: 247 - 255
Published on: Oct 29, 2015
Published by: SAN University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2015 Masaki Murata, Satoshi Ito, Masato Tokuhisa, Qing Ma, published by SAN University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.