TEXT CLASSIFICATION USING LANGUAGE INDEPENDENT DATA  AUGUMENTATION

Dr.V.ANANTHA KRISHNA, KAREWALE SHIVAJYOTH, YERUVU SHREYA, NENAVATH  SHANTHI

TEXT CLASSIFICATION USING LANGUAGE INDEPENDENT DATA AUGUMENTATION

Authors

Dr.V.ANANTHA KRISHNA, KAREWALE SHIVAJYOTH, YERUVU SHREYA, NENAVATH SHANTHI

Keywords:

Text classification, Low resource language, Labeled data, Data augmentation, Synthetic data, Language-independent, Multilingual language model, Sentence embedding, LSTM model, BERT model.

Abstract

Developing a high-performance textclassification model in a low resource language ischallenging due to the lack of labeled data.Meanwhile, collecting large amounts of labeled datais cost inefficient. One approach to increase

References

X. Zhang, J. Zhao, and Y. LeCun, ‘‘Characterlevel convolutional networks for text classification,’’ in Proc. 28th Int. Conf. Neural Inf. Process. Syst.

(NIPS), vol. 1. Cambridge, MA, USA: MIT Press, 2015, pp. 649–657.

J. Mueller and A. Thyagarajan, ‘‘Siamese recurrent architectures for learning sentence similarity,’’ in Proc. 13th AAAI Conf. Artif. Intell.,

, pp. 2786–2792.

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Published

2024-02-15

How to Cite

Dr.V.ANANTHA KRISHNA, KAREWALE SHIVAJYOTH, YERUVU SHREYA, NENAVATH SHANTHI. (2024). TEXT CLASSIFICATION USING LANGUAGE INDEPENDENT DATA AUGUMENTATION . Journal of Computational Analysis and Applications (JoCAAA), 32(2), 299–308. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2704