This paper analyzes the relative importance of different linguistic features for data-driven dependency parsing of Hindi, using a feature pool derived from two state-of-the-art parsers. The analysis shows that the greatest gain in accuracy comes from the addition of morpho-syntactic features related to case, tense, aspect and modality. Combining features from the two parsers, we achieve a labeled attachment score of 76.5%, which is 2 percentage points better than the previous state of the art. We fi-nally provide a detailed error analysis and suggest possible improvements to the parsing scheme.
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