Detecting Complex Sensitive Information via Phrase Structure in Recursive Neural Networks

Jan Neerbek, Ira Assent, Peter Dolog

Publikation: Bidrag til bog/antologi/rapport/proceedingKonferencebidrag i proceedingsForskningpeer review

Abstract

State-of-the-art sensitive information detection in unstructured data relies on the frequency of co-occurrence of keywords with sensitive seed words. In practice, however, this may fail to detect more complex patterns of sensitive information. In this work, we propose learning phrase structures that separate sensitive from non-sensitive documents in recursive neural networks. Our evaluation on real data with human labeled sensitive content shows that our new approach outperforms existing keyword based strategies.

OriginalsprogEngelsk
TitelAdvances in Knowledge Discovery and Data Mining - 22nd Pacific-Asia Conference, PAKDD 2018, Proceedings : PAKDD '18
RedaktørerDinh Phung, Vincent S. Tseng, Geoffrey I. Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi
Antal sider12
Vol/bind10939
ForlagSpringer VS
Publikationsdato2018
Sider373-385
ISBN (Trykt)978-3-319-93039-8
ISBN (Elektronisk)978-3-319-93040-4
DOI
StatusUdgivet - 2018
BegivenhedPacific-Asia Conference on Knowledge Discovery and Data Mining - Melbourne, Australien
Varighed: 3 jun. 20186 jun. 2018
Konferencens nummer: 22
http://prada-research.net/pakdd18

Konference

KonferencePacific-Asia Conference on Knowledge Discovery and Data Mining
Nummer22
Land/OmrådeAustralien
ByMelbourne
Periode03/06/201806/06/2018
Internetadresse
NavnLecture Notes in Computer Science (LNCS)
Nummer10939
ISSN0302-9743

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