An Ensemble Method to Classify Telugu Idiomatic Sentences using Deep Learning Models

Text classification is a requirement for every text processing application because the web contains a vast amount of text data. Intent detection, information extraction, sentiment analysis, and spam detection involves text categorization. Since text classification uses idioms, metaphors, and polysemic words, intent detection can be difficult. It is challenging to automatically identify idioms in Natural Language Processing applications such as Information Retrieval, Machine Translation, and chatbots. In all these applications, automatic idiom recognition is crucial. In this work, idiomatic and literals sentences are being classified. Idioms are typical expressions with new meanings. This research proposes an ensemble model using pretrained deep learning models to make model with more predictive nature. The models are trained and tested using in-house dataset. Moreover, an in-house dataset that contains 1040 idiomatic and literal sentences is suggested. The experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy of 86% on the test dataset.