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                                       Details for article 4 of 10 found articles
 
 
  Deviation detection in text using conceptual graph interchange format and error tolerance dissimilarity function
 
 
Title: Deviation detection in text using conceptual graph interchange format and error tolerance dissimilarity function
Author: Kamaruddin, Siti Sakira
Hamdan, Abdul Razak
Bakar, Azuraliza Abu
Mat Nor, Fauzias
Appeared in: Intelligent data analysis
Paging: Volume 16 (2012) nr. 3 pages 487-511
Year: 2012-05-09
Contents: The rapid increase in the amount of textual data has brought forward a growing research interest towards mining text to detect deviations. Specialized methods for specific domains have emerged to satisfy various needs in discovering rare patterns in text. This paper focuses on a graph-based approach for text representation and presents a novel error tolerance dissimilarity algorithm for deviation detection. We resolve two non-trivial problems, i.e. semantic representation of text and the complexity of graph matching. We employ conceptual graphs interchange format (CGIF) – a knowledge representation formalism to capture the structure and semantics of sentences. We propose a novel error tolerance dissimilarity algorithm to detect deviations in the CGIFs. We evaluate our method in the context of analyzing real world financial statements for identifying deviating performance indicators. We show that our method performs better when compared with two related text based graph similarity measuring methods. Our proposed method has managed to identify deviating sentences and it strongly correlates with expert judgments. Furthermore, it offers error tolerance matching of CGIFs and retains a linear complexity with the increasing number of CGIFs.
Publisher: IOS Press
Source file: Elektronische Wetenschappelijke Tijdschriften
 
 

                             Details for article 4 of 10 found articles
 
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