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Deep learning based Character-level approach for Morphological Inflection Generation

Publication Type : Conference Paper

Thematic Areas : Scientific reports

Authors : Dr. Soman K. P., Vidya Prasad K.; B. Premjith; Chandni Chandran V.; Prabaharan Poornachandran

Source : 2019 International Conference on Intelligent Computing and Control Systems (ICCS), Madurai, India (2019)

Url : https://ieeexplore.ieee.org/abstract/document/9065571

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Computer Science

Year : 2019

Abstract : In this paper, we present our work on morphological inflection generation of Sanskrit using a deep learning approach. Sanskrit is a morphologically rich language which came into use from the Vedic period. A basic understanding of the language formation is needed to study the abundant literature in it. Here a computational model for word formation in Sanskrit is proposed using deep learning based models. They are applied here to attain the morphological changes that a root word undergoes to result in the surface form. The approach is in character level so as to capture the character level transformations. The best performance was obtained from the Bidirectional Gated Recurrent Unit architecture with an accuracy of 98.42% and an F1-Score of 0.9838. This model is purely dependent on the dataset and does not require any external linguistic resources.

Cite this Research Publication : V. Prasad K., B. Premjith, Chandni Chandran V., Dr. Soman K. P., and Prabaharan Poornachandran, “Deep learning based Character-level approach for Morphological Inflection Generation”, in 2019 International Conference on Intelligent Computing and Control Systems (ICCS), Madurai, India, 2019.

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