Intelligent Question Classification Through Machine Learning Techniques

Abstract

The agricultural sector plays a vital role in economic development and depends heavily on timely, accurate, and relevant information for effective decision-making. Farmers, agricultural scientists, and other stakeholders often require quick and reliable answers to queries related to crop management, soil health, pest control, weather conditions, and sustainable farming practices. In recent years, Question-Answering (QA) systems have gained significant attention as a promising technological solution to address this growing demand. These systems allow users to pose questions in natural language and receive precise, context-aware responses.

This research paper presents the conceptualization and development of a QA system specifically tailored for the agricultural domain. The proposed system harnesses the power of Natural Language Processing (NLP) and Machine Learning (ML) to classify questions based on their type. Accurate question classification is essential for retrieving relevant answers and improving the overall performance of the QA system. Several ML algorithms are explored implemented, and compared to determine the most effective model for this task.