Abstract
Automatic word spacing will be a very useful tool in a SMS (simple message service) , if it can be commercially served. However, the problems of implementing it in the devices such as mobile phones are small memory and low computing power of the devices. To tackle these problems, this paper proposes a combined model of rule-based learning and memory-based learning. According to the experimental results, the model shows higher accuracy than rule-based learning or memory-based learning alone. In addition, the generated rules are so small and simple that the proposed model is appropriate for small memory devices.
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.
Similar content being viewed by others
References
Clark, P., Niblett, T.: The CN2 Induction Algorithm. Machine Learning 3(1), 261–284 (1989)
Cohen, W.: Fast Effective Rule Induction. In: Proceedings of the 12th International Conference on Mahcine Learning, pp. 115–123 (1995)
Daelemans, W., Zavrel, J., Sloot, K., Bosch, A.: TiMBL: Tilburg Memory Based Learner, version 4.1, Reference Guide, ILK 01-04, Tilburg University (2001)
Fürnkfranz, J., Widmar, G.: Incremental Reduced Error Pruning. In: Proceedings of the 11th International Conference on Machine Learning, pp. 70–77 (1994)
Kang, S.-S.: Eojeol-Block Bidirectional Algorithm for Automatic Word Spacing of Hangul Sentences. Journal of KISS 27(4), 441–447 (2000) (in Korean)
Kang, S.-S.: Improvement of Automatic Word Segmentation of Korean by Simplifying Syllable Bigram. In: Proceedings of the 15th Conference on Korean Language and Information Processing, pp. 227–231 (2004) (in Korean)
Kim, K.-S., Lee, H.-J., Lee, S.-J.: Three-Stage Spacing System for Korean in Sentence with No Word Boundaries. Journal of KISS 25(12), 1838–1844 (1998) (in Korean)
Lee, D.-G., Lee, S.-Z., Rim, H.-C., Lim, H.-S.: Automatic Word Spacing Using Hidden Markov Model for Refining Korean Text Corpora. In: Proceedings of the 3rd Workshop on Asian Language Resources and International Standardization, pp. 51–57 (2002)
Park, S.-B., Zhang, B.-T.: Text Chunking by Combining Hand-Crafted Rules and Memory-Based Learning. In: Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, pp. 497–504 (2003)
Park, S.-B., Chang, J.-H., Zhang, B.-T.: Korean Compound Noun Decomposition Using Syllabic Information Only. In: Proceedings of the 5th Annual Conference on Intelligent Text Processing and Computational Linguistics, pp. 146–157 (2004)
Quinlan, R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publisher, San Francisco (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Park, SB., Lee, EK., Tae, YS. (2005). Automatic Word Spacing in Korean for Small Memory Devices. In: Ali, M., Esposito, F. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2005. Lecture Notes in Computer Science(), vol 3533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504894_36
Download citation
DOI: https://doi.org/10.1007/11504894_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26551-1
Online ISBN: 978-3-540-31893-4
eBook Packages: Computer ScienceComputer Science (R0)