  Website privacy policies are too long to read and difficult to understand.
The over-sophisticated language makes privacy notices to be less effective than
they should be. People become even less willing to share their personal
information when they perceive the privacy policy as vague. This paper focuses
on decoding 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 vague
terms. The approach holds promise for modeling and understanding language
vagueness.
