Exploiting Maching Learning for Automatic Semantic Feature Assignment

In this paper we experiment with supervised machine learning techniques for the task of assigning semantic categories to nouns in Czech. The experiments work with 16 semantic categories based on available manually annotated data. The paper compares two possible approaches - one based on the contextual information, the other based upon morphological properties - we are trying to automatically extract final segments of lemmas which might carry semantic information. The central problem of this research is finding the features for machine learning that produce better results for relatively small training data size.