A Statistical Approach to Prediction of Empty Categories in Hindi Dependency Treebank

In this paper we use statistical dependency parsing techniques to detect NULL or Empty categories in the Hindi sentences. We have currently worked on Hindi dependency treebank which is released as part of COLING-MTPIL 2012 Workshop. Earlier Rule based approaches are employed to detect Empty heads for Hindi language but statistical learning for automatic prediction is not explored. In this approach we used a technique of introducing complex labels into the data to predict Empty categories in sentences. We have also discussed about shortcomings and difficulties in this approach and evaluated the performance of this approach on different Empty categories.