Abstract
Automatic text chunking is a task which aims to recognize phrase structures in natural language text. It is the key technology of knowledge-based system where phrase structures provide important syntactic information for knowledge representation. Support Vector Machine (SVM-based) phrase chunking system had been shown to achieve high performance for text chunking. But its inefficiency limits the actual use on large dataset that only handles several thousands tokens per second. In this paper, we firstly show that the state-of-the-art performance (94.25) in the CoNLL-2000 shared task based on conventional SVM learning. However, the off-the-shelf SVM classifiers are inefficient when the number of phrase types scales to high. Therefore, we present two novel methods that make the system substantially faster in terms of training and testing while only results in a slightly decrease of system performance. Experimental result shows that our method achieves 94.09 in F rate, which handles 13000 tokens per second in the CoNLL-2000 chunking task.
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
Ando, R.K., Zhang, T.: A high-performance semi-supervised learning method for text chunking. In: Proceedings of 43rd Annual Meetings of the Association for Computational Linguistics, pp. 1–9 (2005)
Brill, E.: Transformation-based error-driven learning and natural language processing: a case study in part of speech tagging. Computational Linguistics 21(4), 543–565 (1995)
Carreras, X., Marquez, L.: Phrase recognition by filtering and ranking with perceptrons. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP) (2003)
Carreras, X., Marquez, L., Castro, J.: Filtering-ranking perceptron learning for partial parsing. Machine Learning Journal 59, 1–31 (2005)
Giménez, J., Márquez, L.: Fast and accurate Part-of-Speech tagging: the SVM approach revisited. In: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP), pp. 158–165 (2003)
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Proceedings of the European Conference on Machine Learning, pp. 137–142 (1998)
Kudoh, T., Matsumoto, Y.: Chunking with support vector machines. In: The Proceedings of the 2nd Meetings of the North American Chapter and the Association for the Computational Linguistics, pp. 192–199 (2001)
Molina, A., Pla, F.: Shallow Parsing using Specialized HMMs. Journal of Machine Learning Research, 595–613 (2002)
Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large margin dags for multiclass classification. Advanced in Neural Information Processing Systems 12, 547–553 (2000)
Ramshaw, L.A., Marcus, M.P.: Text chunking using transformation based learning. In: Proceedings of the 3rd Workshop on Very Large Corpora, pp. 82–94 (1995)
Sagae, K., Lavie, A., MacWhinney, B.: Automatic Measurement of Syntactic Development in Child Language. In: Proceedings of 43rd Annual Meetings of the Association for Computational Linguistics, pp. 197–204 (2005)
Tjong Kim Sang, E.F., Buchholz, S.: Introduction to the CoNLL 2000 shared task: chunking. In: Proceedings of Conference on Natural Language Learning (CoNLL), pp. 127–132 (2000)
Tjong Kim Sang, E.F.: Memory-based shallow parsing. Journal of Machine Learning Research, 559–594 (2002)
Watanabe, T., Sumita, E., Okuno, H.G.: Chunk-based statistical translation. In: Proceedings of 41st Annual Meetings of the Association for Computational Linguistics, pp. 303–310 (2003)
Wu, Y.-C., Chang, C.-H., Lee, Y.-S.: A general and multi-lingual phrase chunking model based on masking method. In: Proceedings of 7th International Conference on Intelligent Text Processing and Computational Linguistics, pp. 144–155 (2006)
Zhang, T., Damerau, F., Johnson, D.: Text Chunking based on a Generalization Winnow. Journal of Machine Learning Research 2, 615–637 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, YC., Yang, JC., Lee, YS., Yen, SJ. (2006). Efficient and Robust Phrase Chunking Using Support Vector Machines. In: Ng, H.T., Leong, MK., Kan, MY., Ji, D. (eds) Information Retrieval Technology. AIRS 2006. Lecture Notes in Computer Science, vol 4182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11880592_27
Download citation
DOI: https://doi.org/10.1007/11880592_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45780-0
Online ISBN: 978-3-540-46237-8
eBook Packages: Computer ScienceComputer Science (R0)