Named Entity Recognition for the Mongolian Language

This paper presents a pioneering work on building a Named Entity Recognition system for the Mongolian language, with an agglutinative morphol- ogy and a subject-object-verb word order. Our work explores the fittest feature set from a wide range of features and a method that refines machine learning ap- proach using gazetteers with approximate string matching, in an effort for robust handling of out-of-vocabulary words. As well as we tried to apply various ex- isting machine learning methods and find optimal ensemble of classifiers based on genetic algorithm. The classifiers uses different feature representations. The resulting system constitutes the first-ever usable software package for Mongolian NER, while our experimental evaluation will also serve as a much-needed basis of comparison for further research.