Adaptive Artificial Intelligent Q&A Platform
This addresses the problem of accessing knowledge from large datasets for users through natural language queries, but it appears incremental as it uses standard components like deep neural networks without novel breakthroughs.
The paper tackles building a question-answering system that processes large datasets to allow users to ask questions in natural language and receive responses, aiming to make interactions feel human-like.
The paper presents an approach to build a question and answer system that is capable of processing the information in a large dataset and allows the user to gain knowledge from this dataset by asking questions in natural language form. Key content of this research covers four dimensions which are; Corpus Preprocessing, Question Preprocessing, Deep Neural Network for Answer Extraction and Answer Generation. The system is capable of understanding the question, responds to the user's query in natural language form as well. The goal is to make the user feel as if they were interacting with a person than a machine.