NDLI: A Study of SVM Kernel Functions for Sensitivity Classification Ensembles with POS Sequences

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A Study of SVM Kernel Functions for Sensitivity Classification Ensembles with POS Sequences

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A Study of SVM Kernel Functions for Sensitivity Classification Ensembles with POS Sequences

Content Provider	ACM Digital Library
Author	Ounis, Iadh García-Pedrajas, Nicolás Macdonald, Craig McDonald, Graham
Abstract	Freedom of Information (FOI) laws legislate that government documents should be opened to the public. However, many government documents contain sensitive information, such as confidential information, that is exempt from release. Therefore, government documents must be sensitivity reviewed prior to release, to identify and close any sensitive information. With the adoption of born-digital documents, such as email, there is a need for automatic sensitivity classification to assist digital sensitivity review. SVM classifiers and Part-of-Speech sequences have separately been shown to be promising for sensitivity classification. However, sequence classification methodologies, and specifically SVM kernel functions, have not been fully investigated for sensitivity classification. Therefore, in this work, we present an evaluation of five SVM kernel functions for sensitivity classification using POS sequences. Moreover, we show that an ensemble classifier that combines POS sequence classification with text classification can significantly improve sensitivity classification effectiveness (+6.09% $F_{2})$ compared with a text classification baseline, according to McNemar's test of significance.
Starting Page	1097
Ending Page	1100
Page Count	4
File Format	PDF
ISBN	9781450350228
DOI	10.1145/3077136.3080731
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Sensitive information Sequence classification Freedom of information Ensemble learning Text classification
Content Type	Text
Resource Type	Article

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