Morpheme Boundary Detection & Grammatical Feature Prediction for Gujarati : Dataset & Model
This provides the first dataset and model for morphological analysis in Gujarati, addressing a gap for this low-resource language.
The paper tackles the challenge of developing NLP resources for the low-resource Gujarati language by creating a morphological analyzer that performs morpheme boundary detection and grammatical feature tagging, achieving effective handling of language morphology without hand-crafted rules.
Developing Natural Language Processing resources for a low resource language is a challenging but essential task. In this paper, we present a Morphological Analyzer for Gujarati. We have used a Bi-Directional LSTM based approach to perform morpheme boundary detection and grammatical feature tagging. We have created a data set of Gujarati words with lemma and grammatical features. The Bi-LSTM based model of Morph Analyzer discussed in the paper handles the language morphology effectively without the knowledge of any hand-crafted suffix rules. To the best of our knowledge, this is the first dataset and morph analyzer model for the Gujarati language which performs both grammatical feature tagging and morpheme boundary detection tasks.