Title

Modeling Language Vagueness In Privacy Policies Using Deep Neural Networks

Abstract

Website privacy policies are too long to read and difficult to understand. The over-sophisticated language undermines the effectiveness of privacy notices. People become less willing to share their personal information when they perceive the privacy policy as vague. The goal of this paper is to decode vagueness from a natural language processing perspective. While thoroughly identifying the vague terms and their linguistic scope remains an elusive challenge, in this work we seek to learn vector representations of words in privacy policies using deep neural networks. The vector representations are fed to an interactive visualization tool (LSTMVis) to test on their ability to discover syntactically and semantically related terms. The approach holds promise for modeling and understanding language vagueness.

Publication Date

1-1-2016

Publication Title

AAAI Fall Symposium - Technical Report

Volume

FS-16-01 - FS-16-05

Number of Pages

257-263

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

85025834631 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85025834631

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