%0 Journal Article
%@ 21693536
%A Abdullah, M.H.A.
%A Aziz, N.
%A Abdulkadir, S.J.
%A Alhussian, H.S.A.
%A Talpur, N.
%D 2023
%F scholars:19363
%I Institute of Electrical and Electronics Engineers Inc.
%J IEEE Access
%K Character recognition; Deep learning; Information retrieval; Natural language processing systems, Deep learning; Events extractions; Information extraction; Named entities; Named entity recognition; Relation extraction; State of the art; Systematic literature review; Text extraction; Textual data, Data mining
%P 10535-10562
%R 10.1109/ACCESS.2023.3240898
%T Systematic Literature Review of Information Extraction From Textual Data: Recent Methods, Applications, Trends, and Challenges
%U https://khub.utp.edu.my/scholars/19363/
%V 11
%X Information extraction (IE) is a challenging task, particularly when dealing with highly heterogeneous data. State-of-the-art data mining technologies struggle to process information from textual data. Therefore, various IE techniques have been developed to enable the use of IE for textual data. However, each technique differs from one another because it is designed for different data types and has different target information to be extracted. This study investigated and described the most contemporary methods for extracting information from textual data, emphasizing their benefits and shortcomings. To provide a holistic view of the domain, this comprehensive systematic literature review employed a systematic mapping process to summarize studies published in the last six years (from 2017 to 2022). It covers fundamental concepts, recent approaches, applications, and trends, in addition to challenges and future research prospects in this domain area. Based on an analysis of 161 selected studies, we found that the state-of-the-art models employ deep learning to extract information from textual data. Finally, this study aimed to guide novice and experienced researchers in future research and serve as a foundation for this research area. © 2013 IEEE.
%Z cited By 7