The nature of statistical learning theory
The nature of statistical learning theory
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Learning noun-modifier semantic relations with corpus-based and WordNet-based features
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Measuring semantic similarity by latent relational analysis
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A concept-centered approach to noun-compound interpretation
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
A knowledge-rich approach to identifying semantic relations between nominals
Information Processing and Management: an International Journal
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We describe a supervised learning approach to categorizing inter-noun relations, based on Support Vector Machines, that builds a different classifier for each of seven semantic relations. Each model uses the same learning strategy, while a simple voting procedure based on five trained discriminators with various blends of features determines the final categorization. The features that characterize each of the noun pairs are a blend of lexical-semantic categories extracted from WordNet and several flavors of syntactic patterns extracted from various corpora, including Wikipedia and the WMTS corpus.