Domain-aware Evaluation of Named Entity Recognition Systems for Croatian

Abstract

We provide an evaluation of the currently available named entity recognition systems for Croatian. The evaluation puts special emphasis on domain dependence. To this goal, we manually annotated a dataset of approximately 1 million tokens of Croatian text from various domains within the newspaper text genre. The dataset was annotated using a three-class named entity tagset – denoting personal names, locations and organizations. We give insight to feature selection, domain sensitivity and effects of increase in training set size for statistical named entity recognition using the state-of-the-art Stanford NER system. We also sketch a comparison of publicly available named entity recognition systems for Croatian considering domain dependence, regardless of their underlying paradigms. Our top-performing system achieved an F1-score of 0.884 in a mixed-domain testing scenario, scoring 0.925 and 0.843 in the two domains separated for the experiment. The system shows consistency in state-of-the-art scores for detecting names of persons, locations and organizations.