The nature of statistical learning theory
The nature of statistical learning theory
A maximum entropy approach to natural language processing
Computational Linguistics
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A probabilistic account of logical metonymy
Computational Linguistics
A comparison of parsing technologies for the biomedical domain
Natural Language Engineering
Efficient support vector classifiers for named entity recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Statistics based hybrid approach to Chinese base phrase identification
CLPW '00 Proceedings of the second workshop on Chinese language processing: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 12
Parsing arguments of nominalizations in English and Chinese
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
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Nominalization is a highly productive phenomenon across languages. The process of nominalization transforms a verb predicate to a referential expression. Identification of nominalizations presents a big challenge to Chinese language processing because there is no morphological difference between a verb nominalization and its corresponding verb predicate. In this paper, we apply a support vector machine to identify nominalizaitons from text. We explore extensively the various nominalization specific classification features for the identification task. Among which, many are first introduced in the literature. The experimental result shows that our method is very effective.