Abstract
In this paper we propose features desirable of linear text segmentation algorithms for the Information Retrieval domain, with emphasis on improving high similarity search of heterogeneous texts. We proceed to describe a robust purely statistical method, based on context overlap exploitation, that exhibits these desired features. Experimental results are presented, along with comparison to other existing algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Choi, F.Y.Y.: Advances in domain independent linear text segmentation. In: Proc. of the North American Chapter of the Association of Computational Linguistic, Seattle, pp. 26–33 (2000)
Choi, F.Y.Y., Wiemer-Hastings, P., Moore, J.: Latent semantic analysis for text segmentation. In: Proc. of the 6th Conference on Empirical Methods in Natural Language Processing, pp. 109–117 (2001)
Foltz, P.W.: Latent Semantic Analysis for text-based research. Behavior Research Methods, Instruments and Computers 28, 197–202 (1996)
Hearst, M.A.: Multi-paragraph segmentation of expository text. In: Proc. of the ACL 1994, Las Crces, NM (1994)
Min-Yen, K., Klavans, J.L., McKeown, K.R.: Linear segmentation and segment significance. In: Proc. of WVLC-6, Montreal, pp. 197–205 (1998)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(675), 788–791 (1999)
Pevzner, L., Hearst, M.: A critique and improvement of an evaluation metric for text segmentation. Computational Linguistics 28(1), 19–36 (2002)
http://sun.stanford.edu/~rmunk/PROPACK/ (March 2007)
Reynar, J.C.: Topic segmentation: Algorithms and applications. Ph.D. thesis, Computer and Information Science, University of Pennsylvania (1998)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Řehůřek, R. (2007). Text Segmentation Using Context Overlap. In: Neves, J., Santos, M.F., Machado, J.M. (eds) Progress in Artificial Intelligence. EPIA 2007. Lecture Notes in Computer Science(), vol 4874. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77002-2_54
Download citation
DOI: https://doi.org/10.1007/978-3-540-77002-2_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77000-8
Online ISBN: 978-3-540-77002-2
eBook Packages: Computer ScienceComputer Science (R0)