A New Approach to Enhance Query Refinement for Marathi Language Information Retrieval

Abstract

The Information Retrieval (IR) enable users to access relevant data in a language different from their queries. This involves the capability to submit a query in one language and retrieve documents in Same or another, like Marathi or English. This is accomplished by creating a system that allows for the comparison of a query in one language with data in same or another language. This research presents a novel approach to enhance query refinement for Marathi language information retrieval, focusing on addressing the language's unique linguistic complexities. The study begins with the collection and preprocessing of a Marathi dataset, emphasizing relevant features. Through comprehensive query analysis, common linguistic variations and ambiguous words in queries are identified. Using natural language processing (NLP) and semantic understanding techniques tailored for Marathi ambiguous words, a robust framework for contextual query interpretation is developed. The methodology includes preprocessing, feature extraction, developing a query refinement model, and building a machine learning model. Performance evaluation of the proposed model is conducted using various metrics such as accuracy of 0.79, mean squared error (MSE) is 0.2077, specificity of 1.0 and sensitivity of 47.54% on the refined queries. The proposed model aims to make a valuable contribution to the field of Marathi language information retrieval.