Text independent language recognition system for indic languages with new features

Sadanandam, M.; Nagesh, Ajay; Prasad, V. Kamakshi; Janaki, V.

doi:10.1109/iccic.2012.6510219

Cited by 2 publications

(1 citation statement)

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“…These resources are exclusively trained on Marathi monolingual corpus. The models are evaluated on downstream Marathi classification and NER tasks work better than their multilingual counterparts [9] [10]. The author of [11] conducted experiments with and evaluated various deep learning models for the task of Marathi text classification.…”

Section: Literature Studymentioning

confidence: 99%

Domain Classification for Marathi Blog Articles using Deep Learning

Kiran N. Girase

2023

IJRITCC

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Nowadays the exponential growth of online content, particularly in the form of blog articles is tremendous, the need for effective techniques to automatically categorize them into relevant domains has become increasingly important. To overcome the challenges the domains like natural language processing (NLP), machine learning (ML) and deep learning (DL)are being working as booster effect to emerge out with solutions. In this proposed system methodology-based NLP and DL domain the long short-term memory (LSTM) classifier for domain classification and compared the existing multiclass classification techniques with having accuracy around 94% and 91% by long short-term memory (LSTM) model using two different data sets one is Marathi new article and another one Financial article data set. The proposed model is being compared with multiple other models like naïve bayes (NB), XGBoost, support vector machine (SVM) and random forest (RF). The final estimated result achieved is best combination of dataset and deep learning algorithm LSTM.

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Section: Literature Studymentioning

confidence: 99%