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- Title
A Novel Ensemble Model for Complex Entities Identification in Low Resource Language.
- Authors
Vats, Preeti; Sharma, Nonita; Sharma, Deepak Kumar
- 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.
- Subjects
INDIA; NATURAL language processing; MACHINE learning; SUPERVISED learning; HINDI language; DEEP learning; RANDOM forest algorithms; DECISION trees
- Publication
EAI Endorsed Transactions on Scalable Information Systems, 2024, Vol 11, Issue 4, p1
- ISSN
2032-9407
- Publication type
Article
- DOI
10.4108/eetsis.4434