Abstract
Abstract: Multi-label classification is the variant of a classification problem where multiple labels are assigned to each instance. In multi-label classification, the training set is composed of instances each associated with a set of labels, and the task is to predict the label sets of unseen instances through analyzing training instances with known label sets. This paper demonstrates the use of multi-label classification to determine tags for news articles written in the Marathi Language of India. The proposed study uses Binary Relevance (One vs Rest) technique of multi-label classification to establish the tags for the given input of a Marathi news article. Tag recommendation systems for Marathi news articles can greatly enhance the user experience for readers and help them find the articles that are most relevant to their interests.
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