Automatic Semantic Relation Extraction with Multiple Boundary Generation

Brandon Beamer, Alla Rozovskaya, Roxana Girju

This paper addresses the task of automatic classification of semantic relations between nouns. We present an improved WordNet-based learning model which relies on the semantic information of the constituent nouns. The representation of each noun's meaning captures conceptual features which play a key role in the identification of the semantic relation. We report substantial improvements over previous WordNet-based methods on the 2007 SemEval data. Moreover, our experiments show that WordNet's IS-A hierarchy is better suited for some semantic relations compared with others. We also compute various learning curves and show that our model does not need a large number of training examples.

Subjects: 13. Natural Language Processing; 12. Machine Learning and Discovery

Submitted: Apr 15, 2008


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