Enhancing Hindi Named Entity Recognition through Ensemble Learning

The main goal of the NER task, a well-known research field, is to identify people, groups, places, and other business entities in text. This task entails classifying and recognising these named things, which is essential for many applications of natural language processing. In this study, we used ensemble approaches to improve the NER task performance. We enhanced outcomes in terms of accuracy, precision, and recall by merging numerous models and their predictions. The ensemble approach offered a stronger and more dependable solution for named entity recognition by successfully addressing the shortcomings of individual models. The outcomes of the experiment showed how the ensemble technique could improve the NER task's overall performance.