One-sided Sampling for Learning Taxonomic Relations in the Modern Greek Economic Domain

This paper describes the process of learning taxonomic relations automatically from Modern Greek economic corpora. Supervised learning (Decision trees, Support Vector Machines, Meta-learning) is applied to economic term pairs; each pair is represented through a set of statistical, semantic and syntactic features. The resulting set of feature-value vectors presents a high imbalance in the class distribution, due to the large number of term pairs that do not present a direct semantic relation. This problem is addressed using One-sided Sampling, which reduces the number of the majority class instances by removing examples that are noisy, misleading or redundant. The approach makes use of no external resources (merely an economic corpus that is annotated with elementary morphological and phrase chunking information) and limited language-dependent elements to facilitate its portability to other languages and domains. An overall f-measure of 71 % is achieved.