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Title: An effective and scalable framework for authorship attribution query processing
Authors: Sarwar, R.
Yu, C. 
Tungare, N.
Chitavisutthivong, K.
Sriratanawilai, S.
Xu, Y.
Chow, D.
Rakthanmanon, T.
Nutanong, S.
Keywords: large scale database
Query processing
similarity search
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Sarwar, R., Yu, C., Tungare, N., Chitavisutthivong, K., Sriratanawilai, S., Xu, Y., Chow, D., Rakthanmanon, T., Nutanong, S. (2018). An effective and scalable framework for authorship attribution query processing. IEEE Access 6 : 50030-50048. ScholarBank@NUS Repository.
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Abstract: Authorship attribution aims at identifying the original author of an anonymous text from a given set of candidate authors and has a wide range of applications. The main challenge in authorship attribution problem is that the real-world applications tend to have hundreds of authors, while each author may have a small number of text samples, e.g., 5-10 texts/author. As a result, building a predictive model that can accurately identify the author of an anonymous text is a challenging task. In fact, existing authorship attribution solutions based on long text focus on application scenarios, where the number of candidate authors is limited to 50. These solutions generally report a significant performance reduction as the number of authors increases. To overcome this challenge, we propose a novel data representation model that captures stylistic variations within each document, which transforms the problem of authorship attribution into a similarity search problem. Based on this data representation model, we also propose a similarity query processing technique that can effectively handle outliers. We assess the accuracy of our proposed method against the state-of-the-art authorship attribution methods using real-world data sets extracted from Project Gutenberg. Our data set contains 3000 novels from 500 authors. Experimental results from this paper show that our method significantly outperforms all competitors. Specifically, as for the closed-set and open-set authorship attribution problems, our method have achieved higher than 95% accuracy. © 2013 IEEE.
Source Title: IEEE Access
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2018.2869198
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
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