A Comprehensive Comparison of Machine Learning Based Methods Used in Bengali Question Classification

TL;DR

This paper compares various machine learning methods for Bengali question classification, analyzing their performance and computational complexity to improve QA systems in the Bengali language.

Contribution

It provides a comprehensive comparison of machine learning approaches for Bengali question classification, highlighting their relative effectiveness and efficiency.

Findings

Different ML methods vary in accuracy and speed.

Some approaches outperform others in specific metrics.

Insights aid in selecting suitable models for Bengali QA systems.

Abstract

QA classification system maps questions asked by humans to an appropriate answer category. A sound question classification (QC) system model is the pre-requisite of a sound QA system. This work demonstrates phases of assembling a QA type classification model. We present a comprehensive comparison (performance and computational complexity) among some machine learning based approaches used in QC for Bengali language.