This Title All WIREs
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 7.250

Text‐based question answering from information retrieval and deep neural network perspectives: A survey

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

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. This article is categorized under: Algorithmic Development > Text Mining Technologies > Machine Learning
Taxonomy of QA techniques provided in this paper
[ Normal View | Magnified View ]
Architecture of RE2 model(Yang et al., 2019)
[ Normal View | Magnified View ]
Architecture of BiMPM‐matching strategies (Wang et al., 2017)
[ Normal View | Magnified View ]
Architecture of Comp‐Clip model (Yoon et al., 2019)
[ Normal View | Magnified View ]
Architecture of CETE model (Laskar et al., 2020)
[ Normal View | Magnified View ]
Computation flow of GSAMN (Lai et al., 2019)
[ Normal View | Magnified View ]
NLP‐capsule framework (Zhao et al., 2019)
[ Normal View | Magnified View ]
General architecture of text‐based QA (Jurafsky & Martin, 2009)
[ Normal View | Magnified View ]

Browse by Topic

Technologies > Machine Learning
Algorithmic Development > Text Mining

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts