Abstract
When a user types in a question in natural language, a Question Answering (QA) system—which is really an Information Retrieval (IR) system—finds the most relevant or near-matching results. This is a result of NLIDB, or the Natural Language Interface to Database. The study delves into the implementation of a Machine Learning-based Hindi Language QA system. There are three stages to the QA system that has been put into place: The first step is to access the natural language query, which involves reading, preprocessing, and tokenizing the input query. Then comes the feature extraction phase, which involves identifying specific feature vectors from the results of the previous phase.