Abstract
The traditional English text chunking approach identifies phrases by using only one model and phrases with the same types of features. It has been shown that the limitations of using only one model are that: the use of the same types of features is not suitable for all phrases, and data sparseness may also result. In this paper, the Distributed Multi-Agent based architecture approach is proposed and applied in the identification of English phrases. This strategy put phrases into agents according to their sensitive features and identifies different phrases in parallel, where the main features are: one, easy and quick communication between phrases; two, avoidance of data sparseness. By applying and testing the approach on the public training and test corpus, the F score for arbitrary phrases identification using Distributed Multi-Agent strategy achieves 95.70% compared to the previous best F score of 94.17%.
This work was supported by the Natural Science Foundation of China (Grant No. 60375019); the International Cooperate Project of Sino-Ireland (Grant No. CI-2003-03).
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Abney, S.: Parsing by chunks. Principle Based Parsing. Berwick, Abney and Tenny. Kluwer A. Publishers, Dordrecht (1991)
Abney, S.: Partial parsing via finite-state cascades. In: Workshop on Robust Parsing, 8th European Summer School in Logic, Language and Information, Prague, Czech Republic, pp. 8–15 (1996)
Skut, W., Brants, T.: A maximum-entropy partial parser for unrestricted text. In: Proceedings of the 6th Workshop on Very Large Corpora., Montreal, Quebec (1998)
Tjong Kim Sang, E.F.: Introduction to the CoNLL-2000 Shared Task: Chunking. In: Proceedings of CoNLL-2000 and LLL-2000, Lisbon, Portugal, pp. 127–132 (2000)
Kudoh, T., Matsumoto, Y.: Use of Support Vector Learning for Chunk Identification. In: Proceedings of CoNLL-2000 and LLL-2000, Lisbon, Portugal, pp. 127–132 (2000)
Tjong Kim Sang, E.F.: Memory-Based Shallow Parsing. In: Proceedings of CoNLL 2000 and LLL 2000, Lisbon, Portugal, pp. 559–594 (2000)
Zhang, T., Damerau, F., Johnson, D.: Text Chunking based on a Generalization of Winnow. Machine Learning Research 2, 615–637 (2002)
Sun, H., Yu, S.: The summarization of shallow parsing method. The linguistics of the present age, 063-073 (2000)
Michael, W.: An Introduction to Multi-Agent Systems, Chichester, England (2002)
Kargupta, H., Hamzaoglu, I., Stafford, B.: Web Based Parallel/Distributed Medical Data Mining Using Software Agents. In: Extended Proceedings of the 1997 Fall Symposium, American Medical Informatics Association (1997)
Liang, Y., Zhao, T.: The Identification of English Base Noun Phrase Based on the Hybrid Strategy. Computer Engineering and Application 35, 1–4 (2004)
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
Liang, YH., Zhao, TJ. (2005). Distributed English Text Chunking Using Multi-agent Based Architecture. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_76
Download citation
DOI: https://doi.org/10.1007/11579427_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29896-0
Online ISBN: 978-3-540-31653-4
eBook Packages: Computer ScienceComputer Science (R0)