Abstract
Text segmentation is a fundamental problem in natural language processing, which has application in information retrieval, question answering, and text summarization. Almost previous works on unsupervised text segmentation are based on the assumption of lexical cohesion, which is indicated by relations between words in the two units of text. However, they only take into account the reiteration, which is a category of lexical cohesion, such as word repetition, synonym or superordinate. In this research, we investigate the non-systematic semantic relation, which is classified as collocation in lexical cohesion. This relation holds between two words or phrases in a discourse when they pertain to a particular theme or topic. This relation has been recognized via a topic model, which is, in turn, acquired from a large collection of texts. The experimental results on the public dataset show the advantages of our approach in comparison to the available unsupervised approaches.
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References
Barzilay, R., Elhadad, M.: Using lexical chains for text summarization. In: Proceedings of ISTS 1997, Madrid, Spain, pp. 10–17 (1997)
Beeferman, D., Berger, A., Lafferty, J.: Statistical models for text segmentation. Machine Learning 34(1-3), 177–210 (1999)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Machine Learning Research 3, 993–1022 (2003)
Choi, F.Y.Y.: Advances in domain independent linear text segmentation. In: Proceedings of NAACL 2000, Seattle, USA, pp. 26–33 (2000)
Choi, F.Y.Y., Wiemer-Hastings, P., Moore, J.: Latent semantic analysis for text segmentation. In: Lee, L., Harman, D. (eds.) Proceedings of EMNLP 2001, pp. 109–117 (2001)
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. The American Society for Information Science 41, 391–407 (1990)
Eisenstein, J., Barzilay, R.: Bayesian unsupervised topic segmentation. In: Proceedings of EMNLP 2008, Honolulu, Hawaii, pp. 334–343 (2008)
Ferret, O.: Finding document topics for improving topic segmentation. In: Proceedings of ACL 2007, Prague, Czech Republic, pp. 480–487 (2007)
Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proceedings of the National Academy of Sciences 101(suppl. 1), 5228–5235 (2004)
Halliday, M.A.K., Hasan, R.: Cohesion in English. Longman Pub. Group, Harlow (1976)
Hearst, M.A.: Multi-paragraph segmentation of expository text. In: Proceedings of ACL 1994, Las Cruces, New Mexico, USA, pp. 9–16 (1994)
Heinrich, G.: Parameter estimation for text analysis. Tech. rep., University of Leipzig, Germany (2005)
Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of SIGIR 1999, pp. 50–57 (1999)
Ji, X., Zha, H.: Domain-independent text segmentation using anisotropic diffusion and dynamic programming. In: Proceedings of SIGIR 2003, Toronto, Canada, pp. 322–329 (2003)
Jurafsky, D., Martin, J.H.: Speech and Language Processing, 2nd edn. Prentice Hall, Englewood Cliffs (2008)
Malioutov, I., Barzilay, R.: Minimum cut model for spoken lecture segmentation. In: Proceedings of COLING-ACL 2006, Sydney, Australia, pp. 25–32 (2006)
Manning, C.D., Schuetze, H.: Foundations of Statistical Natural Language Processing. The MIT Press, Cambridge (1999)
Misra, H., Yvon, F., Jose, J.M., Cappe, O.: Text segmentation via topic modeling: an analytical study. In: Proceedings of CIKM 2009, pp. 1553–1556 (2009)
Morris, J., Hirst, G.: Lexical cohesion computed by thesaural relations as an indicator of the structure of text. Computational Linguistics 17(1), 21–48 (1991)
Otterbacher, J., Radev, D., Kareem, O.: Hierarchical summarization for delivering information to mobile devices. Information Processing and Management 44(2), 931–947 (2008)
Pevzner, L., Hearst, M.A.: A critique and improvement of an evaluation metric for text segmentation. Computational Linguistics 28(1), 19–36 (2002)
Reynar, J.C.: Topic Segmentation: Algorithms and Applications. Ph.D. thesis, University of Pennsylvania (1998)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)
Utiyama, M., Isahara, H.: A statistical model for domain-independent text segmentation. In: Proceedings of ACL 2001, Toulouse, France, pp. 499–506 (2001)
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Nguyen, V.C., Nguyen, L.M., Shimazu, A. (2011). Improving Text Segmentation with Non-systematic Semantic Relation. In: Gelbukh, A.F. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6608. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19400-9_24
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DOI: https://doi.org/10.1007/978-3-642-19400-9_24
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