LiDA Language?Independent Data Augmentation for Text Classification

Project Code :TCMAPY1151

Objective

Our objective is to validate LiDA's effectiveness in language-independent text classification through experiments across diverse languages. We aim to conduct comparative analyses with traditional methods to showcase LiDA's superior performance in handling linguistic variations and achieving robust classification accuracy across multilingual datasets.

Abstract

LiDA presents a novel approach, utilizing LSTM and BERT algorithms for text classification, transcending language barriers. By leveraging deep learning techniques, it achieves robust classification performance across various languages. Key contributions include a language-independent data augmentation strategy, enhancing model generalization. Experimentation demonstrates superior performance compared to traditional methods, showcasing its effectiveness in real-world scenarios.

Keywords: LiDA, text classification, LSTM, BERT, language-independent, data augmentation.

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