#5510. Text-based question answering from information retrieval and deep neural network perspectives: A survey

August 2026publication date
Proposal available till 20-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Computer Science Applications;
Information Systems;
Computer Networks and Communications;
Places in the authors’ list:
place 1place 2place 3place 4
FreeFreeFreeFree
2350 $1200 $1050 $900 $
Contract №5510.1 Contract №5510.2 Contract №5510.3 Contract №5510.4
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)

Abstract:
Text-based question answering (QA) is a challenging task which aims at finding short concrete answers for users questions. This line of research has been widely studied with information retrieval (IR) techniques and has received increasing attention in recent years by considering deep neural network approaches. Deep learning (DL) approaches, which are the main focus of this paper, provide a powerful technique to learn multiple layers of representations and interaction between the questions and the answer sentences. In this paper, we provide a comprehensive overview of different models proposed for the QA task, including both a traditional IR perspective and a more recent deep neural network environment. We also introduce well-known datasets for the task and present available results from the literature to have a comparison between different techniques.
Keywords:
deep learning; information retrieval; text-based question answering

Contacts :
0