A Novel Ensemble Model for Complex Entities Identification in Low Resource Language

Preeti Vats; Nonita Sharma; Deepak Kumar Sharma

doi:10.4108/eetsis.4434

Authors

Preeti Vats Indira Gandhi Delhi Technical University for Women
Nonita Sharma Indira Gandhi Delhi Technical University for Women
Deepak Kumar Sharma Indira Gandhi Delhi Technical University for Women

DOI:

https://doi.org/10.4108/eetsis.4434

Keywords:

NLP, Ensemble learning, Decision Tree, Hindi Text Identification

Abstract

The fundamental method for pre-processing speech or text data that enables computers to comprehend human language is known as natural language processing. Numerous models have been developed to date to pre-process data in the English language; however, the Hindi language does not support these models. India's national tongue is Hindi. In order to help the locals, the authors of this study used supervised learning methods like Linear Regression, SVM, and Naive Bayes algorithm to investigate a dataset of complicated terms in the Hindi language. Additionally, a sophisticated Hindi word classification model is suggested employing several methods based on the forecasts as well as collective learning strategies like Random Forest, Adaboost, and Decision Tree. Depending on how well the user's language is understood, the suggested model will assist in simplifying Hindi text. Authors attempt to classify the uncharted dataset using deep learning algorithms like Bi-LSTM and GRU approaches in further processing.

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