Automatic Semantic Relation Extraction with Multiple Boundary Generation

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.

Authors

• Brandon Beamer Alla Rozovskaya

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