Applying dependency relations to definition extraction

Abstract

Definition Extraction (DE) is the task to automatically identify definitional knowledge in naturally-occurring text. This task has applications in ontology generation, glossary creation or question answering. Although the traditional approach to DE has been based on hand-crafted pattern-matching rules, recent methods incorporate learning algorithms in order to classify sentences as definitional or non-definitional. This paper presents a supervised approach to Definition Extraction in which only syntactic features derived from dependency relations are used. We model the problem as a classification task where each sentence has to be classified as being or not definitional. We compare our results with two well-known approaches: First, a supervised method based on Word-Class Lattices and second, an unsupervised approach based on mining recurrent patterns. Our competitive results suggest that syntactic information alone can contribute substantially to the development and improvement of DE systems.