  Natural Language Processing (NLP) has emerged as a crucial technology for
understanding and generating human language, playing an essential role in tasks
such as machine translation, sentiment analysis, and more pertinently, question
classification. As a subfield within NLP, question classification focuses on
determining the type of information being sought, a fundamental step for
downstream applications like question answering systems. This study presents an
innovative ensemble approach for question classification, combining the
strengths of Electra, GloVe, and LSTM models. Rigorously tested on the
well-regarded TREC dataset, the model demonstrates how the integration of these
disparate technologies can lead to superior results. Electra brings in its
transformer-based capabilities for complex language understanding, GloVe offers
global vector representations for capturing word-level semantics, and LSTM
contributes its sequence learning abilities to model long-term dependencies. By
fusing these elements strategically, our ensemble model delivers a robust and
efficient solution for the complex task of question classification. Through
rigorous comparisons with well-known models like BERT, RoBERTa, and DistilBERT,
the ensemble approach verifies its effectiveness by attaining an 80% accuracy
score on the test dataset.
