@inproceedings{baxi-bhatt-2021-morpheme,
title = "Morpheme boundary Detection {\&} Grammatical feature Prediction for {G}ujarati : Dataset {\&} Model",
author = "Baxi, Jatayu and
Bhatt, Brijesh",
editor = "Bandyopadhyay, Sivaji and
Devi, Sobha Lalitha and
Bhattacharyya, Pushpak",
booktitle = "Proceedings of the 18th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2021",
address = "National Institute of Technology Silchar, Silchar, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2021.icon-main.45",
pages = "369--377",
abstract = "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.",
}
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%0 Conference Proceedings
%T Morpheme boundary Detection & Grammatical feature Prediction for Gujarati : Dataset & Model
%A Baxi, Jatayu
%A Bhatt, Brijesh
%Y Bandyopadhyay, Sivaji
%Y Devi, Sobha Lalitha
%Y Bhattacharyya, Pushpak
%S Proceedings of the 18th International Conference on Natural Language Processing (ICON)
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C National Institute of Technology Silchar, Silchar, India
%F baxi-bhatt-2021-morpheme
%X 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.
%U https://aclanthology.org/2021.icon-main.45
%P 369-377
Markdown (Informal)
[Morpheme boundary Detection & Grammatical feature Prediction for Gujarati : Dataset & Model](https://aclanthology.org/2021.icon-main.45) (Baxi & Bhatt, ICON 2021)
ACL