Abstract
To conduct content analysis over text data, one may look out for important named objects and entities that refer to real world instances, synthesizing them into knowledge relevant to a given information seeking task. In this paper, we introduce a visual analytics tool called ER-Explorer to support such an analysis task. ER-Explorer consists of a data model known as TUBE and a set of data manipulation operations specially designed for examining entities and relationships in text. As part of TUBE, a set of interestingness measures is defined to help exploring entities and their relationships. We illustrate the use of ER-Explorer in performing the task of finding associations between two given entities over a text data collection.
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Dai, H., Lim, EP., Lauw, H.W., Pang, H. (2008). Visual Analytics for Supporting Entity Relationship Discovery on Text Data. In: Yang, C.C., et al. Intelligence and Security Informatics. ISI 2008. Lecture Notes in Computer Science, vol 5075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69304-8_19
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DOI: https://doi.org/10.1007/978-3-540-69304-8_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69136-5
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