Supervised Learning of Syntactic Contexts for Uncovering Definitions and Extracting Hypernym Relations in Text Databases

In this paper we address the problem of automatically constructing structured knowledge from plain texts. In particular, we present a supervised learning technique to first identify definitions in text data, while then finding hypernym relations within them making use of extracted syntactic structures. Instead of using pattern matching methods that rely on lexico-syntactic patterns, we propose a method which only uses syntactic dependencies between terms extracted with a syntactic parser. Our assumption is that syntax is more robust than patterns when coping with the length and the complexity of the texts. Then, we transform the syntactic contexts of each noun in a coarse-grained textual representation, that is later fed into hyponym/hypernym-centered Support Vector Machine classifiers. The results on an annotated dataset of definitional sentences demonstrate the validity of our approach overtaking the current state of the art.