Abstract
Recently, with the development of Chinese semantically annotated corpora, e.g. the Chinese Proposition Bank, the Chinese semantic role labeling (SRL) has been boosted. However, the Chinese SRL researchers now focus on the transplant of existing statistical machine learning methods which have been proven to be effective on English. In this paper, we have established a semantic chunking based method which is different from the traditional ones. Semantic chunking is named because of its similarity with syntactic chunking. The difference is that semantic chunking is used to identify the semantic chunks, i.e. the semantic roles. Based on semantic chunking, the process of SRL is changed from “parsing – semantic role identification – semantic role classification”, to “semantic chunk identification – semantic chunk classification”. With the elimination of the parsing stage, the SRL task can get rid of the dependency on parsing, which is the bottleneck both of speed and precision. The experiments have shown that the semantic chunking based method outperforms previously best-reported results on Chinese SRL and saves a large amount of time.
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
Gildea, D., Jurafsky, D.: Automatic labeling of semantic roles. Computational Linguistics 28(3), 245–288 (2002)
Narayanan, S., Harabagiu, S.: Question answering based on semantic structures. In: The 20th International Conference on Computational Linguistics, Geneva, Switzerland (2004)
Boas, H.C.: Bilingual FrameNet dictionaries for machine translation. In: The International Conference on Language Resources and Evaluation, Las Palmas, Spain (2002)
Carreras, X., Màrquez, L.: Introduction to the conll-2004 shared task: Semantic role labeling. In: The Eighth Conference on Natural Language Learning, Boston, Massachusetts, USA (2004)
Carreras, X., Màrquez, L.: Introduction to the conll-2005 shared task: Semantic role labeling. In: The Nineth Conference on Natural Language Learning, Ann Arbor, Michigan, USA (2005)
Sun, H., Jurafsky, D.: Shallow Semantic Parsing of Chinese. In: The Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Boston, Massachusetts, USA (2004)
Xue, N., Palmer, M.: Automatic semantic role labeling for Chinese verbs. In: The 19th International Joint Conference on Artificial Intelligence, Edinburgh, Scotland (2005)
Xue, N.: Labeling Chinese predicates with semantic roles. Computational linguistics 34(2), 225–255 (2008)
Xue, N., Palmer, M.: Annotating the Propositions in the Penn Chinese Treebank. In: The 2nd SIGHAN Workshop on Chinese Language Processing, Sapporo, Japan (2003)
Xue, N., Xia, F., Chiou, F., Palmer, M.: The Penn Chinese TreeBank: Phrase Structure Annotation of a Large Corpus. Natural Language Engineering 11(2), 207–238 (2005)
Hacioglu, K., Ward, W.: Target word detection and semantic role chunking using support vector machines. In: The Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Edmonton, Canada (2003)
Wang, R., Chi, Z., Wang, X., Wu, T.: An algorithm for semantic chunk identification of Chinese sentences. In: The Tenth IASTED International Conference on Intelligent Systems and Control, Cambridge, Massachusetts, USA (2007)
Ramshaw, L.A., Marcus, M.P.: Text chunking using transformation-based learning. In: The 3rd Workshop on Very Large Corpora, Cambridge, Massachusetts, USA (2005)
Sang, E., Kim, T., Veenstra, J.: Representing text chunks. In: The 38th Annual Meeting of the Association for Computational Linguistics, Hong Kong, China (1999)
Uchimoto, K., Ma, Q., Murata, M., Ozaku, H., Isahara, H.: Named Entity Extraction Based on A Maximum Entropy Model and Transformation Rules. In: The 38th Annual Meeting of the Association for Computational Linguistics, Hong Kong, China (2000)
Kudo, T., Matsumoto, Y.: Chunking with Support Vector Machines. In: Second Meeting of North American Chapter of the Association for Computational Linguistics, Pittsburgh, USA (2001)
Lafferty, J., McCallum, A., Pereira, F.: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: The 18th International Conference on Machine Learning, Williamstown, MA, USA (2001)
Wang, H., Zhan, W., Yu, S.: The Specification of The Semantic Knowledge-base of Contemporary Chinese. Journal of Chinese Language and Computing 13(2), 159–176 (2003)
Collins, M.: Head-Driven Statistical Models for Natural Language Parsing. Pennsylvania University (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ding, W., Chang, B. (2009). Fast Semantic Role Labeling for Chinese Based on Semantic Chunking. In: Li, W., Mollá-Aliod, D. (eds) Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy. ICCPOL 2009. Lecture Notes in Computer Science(), vol 5459. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00831-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-00831-3_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00830-6
Online ISBN: 978-3-642-00831-3
eBook Packages: Computer ScienceComputer Science (R0)