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In some languages, gender is a grammatical property of the noun. Grammatical gender identification enhances machine translation of such languages. This paper reports a three staged approach for grammatical gender identification that makes use of word and morphological features only. A Morphological Analyzer is used to extract the morphological features. In stage one, association rule mining is used to obtain grammatical gender identification rules. Classification is used at the second stage to identify grammatical gender for nouns that are not covered by grammatical gender identification rules obtained in stage one. The third stage combines the results of the two stages to identify the gender. The staged approach has a better precision, recall and F-score compared to machine learning classifiers used on complete data set. The approach was tested on Konkani nouns extracted from the Konkani WordNet and an F-Score 0.84 was obtained.

Staged Approach for Grammatical Gender Identification of Nouns using Association Rule Mining and Classification