Open AccessFindThatQuote: A Question-Answering Web-based System to Locate Quotes using Deep Learning and Natural-Language Processing
Author(s) -
Nathan T. Ji,
Yu Sun
Publication year - 2021
Publication title -
computer science and information technology ( cs and it )
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5121/csit.2021.110909
Subject(s) - relevance (law) , multitude , computer science , question answering , world wide web , natural (archaeology) , natural language , information retrieval , data science , artificial intelligence , history , philosophy , archaeology , epistemology , political science , law
The digital age gives us access to a multitude of both information and mediums in which we can interpret information. A majority of the time, many people find interpreting such information difficult as the medium may not be as user friendly as possible. This project has examined the inquiry of how one can identify specific information in a given text based on a question. This inquiry is intended to streamline one's ability to determine the relevance of a given text relative to his objective. The project has an overall 80% success rate given 10 articles with three questions asked per article. This success rate indicates that this project is likely applicable to those who are asking for content level questions within an article.