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.
Bibliographical noteThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
FunderThis 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 3002, and in part by the Key Projects of Science and Technology of Guizhou Province under Grant  1Z055.
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.
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- Event detection
- Event recognition
- Information extraction
- Information retrieval
ASJC Scopus subject areas
- Computer Science(all)
- Materials Science(all)