Abstract
In the past years much research has been done on data-driven dependency parsing and performance has increased steadily. Dependency grammar has an important inherent characteristic, that is, the nodes closer to root usually make more contribution to audiences than the others. However, that is ignored in previous research in which every node in a dependency structure is considered to play the same role. In this paper a parser based on weighted hierarchical structure learning is proposed to simulate shallow dependency parsing, which has the preference for nodes closer to root during learning. The experimental results show that the accuracies of nodes closer to root are improved at the cost of a little decrease of accuracies of nodes far from root.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Collins, M.: Head-driven statistical models for natural language parsing. Computational Linguistics 29(4), 589–637 (2003)
Collins, M., Ramshaw, L., Haji, Ccirc, J., Tillmann, C.: A statistical parser for czech. In: Proceedings of the 37th conference on Association for Computational Linguistics, pp. 505–512 (1999)
Eisner, J.: Three new probabilistic models for dependency parsing: An exploration. In: Proceedings of the 16th International Conference on Computational Linguistics (COLING 1996), pp. 340–345 (1996)
Jurafsky, D., Martin, J.H.: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. MIT Press, Cambridge (2000)
McDonald, R., Crammer, K., Pereira, F.: Online large-margin training of dependency parsers. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 91–98 (2005)
Nivre, J., Hall, J., Nilsson, J.: Maltparser: A data-driven parser-generator for dependency parsing. In: Proc. of LREC 2006 (2006)
Yamada, H., Matsumoto, Y.: Statistical dependency analysis with support vector machines. In: Proc. IWPT (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kang, Z., Chen, C., Bu, J., Huang, P., Qiu, G. (2008). A Simulated Shallow Dependency Parser Based on Weighted Hierarchical Structure Learning. In: Li, H., Liu, T., Ma, WY., Sakai, T., Wong, KF., Zhou, G. (eds) Information Retrieval Technology. AIRS 2008. Lecture Notes in Computer Science, vol 4993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68636-1_52
Download citation
DOI: https://doi.org/10.1007/978-3-540-68636-1_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68633-0
Online ISBN: 978-3-540-68636-1
eBook Packages: Computer ScienceComputer Science (R0)