A history and theory of textual event detection and recognition

Yanping Chen, Zehua Ding, Qinghua Zheng, Yongbin Qin, Ruizhang Huang, Nazaraf Shah

    Research output: Contribution to journalArticlepeer-review

    6 Citations (Scopus)
    61 Downloads (Pure)


    There is large and growing amounts of textual data that contains information about human activities. Mining interesting knowledge from this textual data is a challenging task because it consists of unstructured or semistructured text that are written in natural language. In the field of artificial intelligence, event-oriented techniques are helpful in addressing this problem, where information retrieval (IR), information extraction (IE) and graph methods (GMs) are three of the most important paradigms in supporting event-oriented processing. In recent years, due to information explosions, textual event detection and recognition have received extensive research attention and achieved great success. Many surveys have been conducted to retrospectively assess the development of event detection. However, until now, all of these surveys have focused on only a single aspect of IR, IE or GMs. There is no research that provides a complete introduction or a comparison of IR, IE, and GMs. In this article, a survey about these techniques is provided from a broader perspective, and a convenient and comprehensive comparison of these techniques is given. The hallmark of this article is that it is the first survey that combines IR, IE and GMs in a single frame and will therefore benefit researchers by acting as a reference in this field.

    Original languageEnglish
    Pages (from-to)201371-201392
    Number of pages22
    JournalIEEE Access
    Early online date30 Oct 2020
    Publication statusPublished - 17 Nov 2020

    Bibliographical note

    This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/


    This work was supported in part by the National Natural Science Foundation of China through the Joint Funds under Grant U1836205, in part by the National Natural Science Foundation of China through the Major Research Program under Grant 91746116, in part by the National Natural Science Foundation of China under Grant 62066007 and Grant 62066008, in part by the Major Special Science and Technology Projects of Guizhou Province under Grant [2017]3002, and in part by the Key Projects of Science and Technology of Guizhou Province under Grant [2020] 1Z055.

    Funding Information:
    During the 1990s, the MUC was supported by the Science Applications International Corporation (SAIC) to foster the development of novel and improved methods for IE. The MUC saw the development of information extraction. In the first MUC, there was no definition for the format of the output and evaluation criterion. Participants were free to determine the output format according to their understanding of the task. Then, the community summarized the results and defined the direction of the following conference. The MUC-2 crystallized event recognition as frame filling tasks, whereas the MUC-6 coined the task of ‘‘named entity’’ recognition to support sophisticated extraction tasks. From 1987 to 1997, the MUC was held seven times and was then replaced by the ACE program.

    Publisher Copyright:
    © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.


    • Event detection
    • Event recognition
    • Information extraction
    • Information retrieval

    ASJC Scopus subject areas

    • Computer Science(all)
    • Materials Science(all)
    • Engineering(all)


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