Evaluation of Two Bengali Dependency Parsers (original) (raw)

In this paper we have addressed two dependency parsers for a free-word order Indian language, namely Bengali. One of the parsers is a grammar-driven one whereas the second parser is a datadriven one. The grammar-driven parser is an extension of a previously developed parser whereas the data driven parser is the MaltParser customized for Bengali. Both the parsers are evaluated on two datasets: ICON NLP Tool Contest data and Dataset-II (developed by us). The evaluation shows that the grammar-based parser outperforms the MaltParser on ICON data based on which the demand frames of the Bengali verbs were developed but its performance degrades while dealing with completely unknown data, i.e. dataset-II. However, MaltParser performs better on dataset-II and the whole data. Evaluation and error analysis further reveals that the parsers show some complimentary capabilities, which indicates a future scope for their integration to improve the overall parsing efficiency.

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