Abstract
In many contexts today, documents are available in a number of versions. In addition to explicit knowledge that can be queried/searched in documents, these documents also contain implicit knowledge that can be found by text mining. In this paper we will study association rule mining of temporal document collections, and extend previous work within the area by 1) performing mining based on semantics as well as 2) studying the impact of appropriate techniques for ranking of rules.
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Nørvåg, K., Fivelstad, O.K. (2009). Semantic-Based Temporal Text-Rule Mining. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2009. Lecture Notes in Computer Science, vol 5449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00382-0_36
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DOI: https://doi.org/10.1007/978-3-642-00382-0_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00381-3
Online ISBN: 978-3-642-00382-0
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