Towards Never Ending Language Learning for Morphologically Rich Languages
Kseniya Buraya | Lidia Pivovarova | Sergey Budkov | Andrey Filchenkov
Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing
This work deals with ontology learning from unstructured Russian text. We implement one of components Never Ending Language Learner and introduce the algorithm extensions aimed to gather specificity of morphologicaly rich free-word-order language. We demonstrate that this method may be successfully applied to Russian data. In addition we perform several additional experiments comparing different settings of the training process. We demonstrate that utilizing of morphological features significantly improves the system precision while using of seed patterns helps to improve the coverage.