Survey of Temporal Information Extraction


Chae-Gyun Lim, Young-Seob Jeong, Ho-Jin Choi, Journal of Information Processing Systems Vol. 15, No. 4, pp. 931-956, Aug. 2019  

10.3745/JIPS.04.0129
Keywords: Annotation Language, Temporal Information, Temporal Information Extraction
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Abstract

Documents contain information that can be used for various applications, such as question answering (QA) system, information retrieval (IR) system, and recommendation system. To use the information, it is necessary to develop a method of extracting such information from the documents written in a form of natural language. There are several kinds of the information (e.g., temporal information, spatial information, semantic role information), where different kinds of information will be extracted with different methods. In this paper, the existing studies about the methods of extracting the temporal information are reported and several related issues are discussed. The issues are about the task boundary of the temporal information extraction, the history of the annotation languages and shared tasks, the research issues, the applications using the temporal information, and evaluation metrics. Although the history of the tasks of temporal information extraction is not long, there have been many studies that tried various methods. This paper gives which approach is known to be the better way of extracting a particular part of the temporal information, and also provides a future research direction.


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Cite this article
[APA Style]
Chae-Gyun Lim, Young-Seob Jeong, & Ho-Jin Choi (2019). Survey of Temporal Information Extraction. Journal of Information Processing Systems, 15(4), 931-956. DOI: 10.3745/JIPS.04.0129.

[IEEE Style]
C. Lim, Y. Jeong and H. Choi, "Survey of Temporal Information Extraction," Journal of Information Processing Systems, vol. 15, no. 4, pp. 931-956, 2019. DOI: 10.3745/JIPS.04.0129.

[ACM Style]
Chae-Gyun Lim, Young-Seob Jeong, and Ho-Jin Choi. 2019. Survey of Temporal Information Extraction. Journal of Information Processing Systems, 15, 4, (2019), 931-956. DOI: 10.3745/JIPS.04.0129.