Evaluation of Two Bengali Dependency Parsers
Arjun Das, Arabinda Shee, Utpal Garain · 2012
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 wherea s the second parser is a datadriven one. The grammar-driven parser is an extension of a previously developed p arser 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 it s performance degrades while dealing with completely unknown data, i.e. dataset-II. However, MaltParser perf orms better on dataset-II and the whole data . Evaluation and error analysis further reveals that the parsers sho w some complimentary capabilities, which indicates a future scope for their integ ration to improve the overall parsing efficiency.