Question Categorizer: An Automated Approach For Organizing Queries

Question categorization is an essential task in natural language processing (NLP) and information retrieval which is the process of automatically assigning a category or class label to a given question based on its content. Categorizing questions plays a pivotal role in enhancing comprehension and facilitating targeted responses. This paper delves into the development and implementation of an advanced question categorization system which classifies questions into eight distinct categories: Analytical, definition, Theoretical, Inferential, Formulation, Differential, Mathematical and Statistical thereby encompassing a wide spectrum of question types. The foundation of this system lies in the integration of a sophisticated BERT architecture, natural language processing techniques, and domain-specific expertise. The system autonomously processes input queries, identifying the underlying characteristics that define each category and assigns the category to the input data. The model is trained on a curated set of data and the proposed methodology achieved promising results with an accuracy score of 0.98 for the 8 categories multiclass classification.