Ranking based Question Answering System with a Web and Mobile Application

This work attempts to comprehend the fundamental working of the deep learning models that have been proposed till now in order to arrive at an inventive and accurate ensemble model for handling questions which are either unanswerable or have answers as persistent content from the context. In general cases of Natural Language Processing applications, Long Short-Term Memory and Gated Recurrent Units have shown great performance. With the introduction of Convolutional Neural Networks (which were conventionally used for performing image analysis and object/landmark detection) in the domain of text analysis along with the latest Attention mechanisms substantial progress in this domain was observed. On the SQUAD dataset, these models can learn to train on all the possible varieties of questions that may exist. An ensemble model based on the BERT encoder was also implemented for the Machine Reading Comprehension task. This work presents a ranking based question answering system. The complete system was divided into three modules namely Extension, Question selection Web Service and QA system. Later a chronic extension and a mobile app is developed which presents the above approach.