- Title
- Automated unsupervised authorship analysis using evidence accumulation clustering
- Creator
- Layton, Robert; Watters, Paul; Dazeley, Richard
- Date
- 2013
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/63717
- Identifier
- vital:4856
- Identifier
-
https://doi.org/10.1017/S1351324911000313
- Identifier
- ISSN:1351-3249
- Abstract
- Authorship Analysis aims to extract information about the authorship of documents from features within those documents. Typically, this is performed as a classification task with the aim of identifying the author of a document, given a set of documents of known authorship. Alternatively, unsupervised methods have been developed primarily as visualisation tools to assist the manual discovery of clusters of authorship within a corpus by analysts. However, there is a need in many fields for more sophisticated unsupervised methods to automate the discovery, profiling and organisation of related information through clustering of documents by authorship. An automated and unsupervised methodology for clustering documents by authorship is proposed in this paper. The methodology is named NUANCE, for n-gram Unsupervised Automated Natural Cluster Ensemble. Testing indicates that the derived clusters have a strong correlation to the true authorship of unseen documents. © 2011 Cambridge University Press.
- Relation
- Natural Language Engineering Vol. 19, no. 1 (2013), p. 95-120
- Rights
- Copyright 2011 Cambridge Unviersity Press
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 1702 Cognitive Science; 2004 Linguistics; Authorship analysis; Classification tasks; Clustering documents; Evidence accumulation; Natural clusters; Strong correlation; Unsupervised method; Automation; Information retrieval systems
- Full Text
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