Towards Never Ending Language Learning for Morphologically Rich Languages

Abstract

This work deals with ontology learning from unstructured Russian text. We implement one of the components of Never Ending Language Learner and introduce the algorithm extensions aimed to gather specificity of morphologically rich freeword-order language. We perform several experiments comparing different settings of the training process. We demonstrate that morphological features significantly improve the system precision while seed patterns help to improve the coverage.