Date of Original Version

6-2014

Type

Conference Proceeding

Journal Title

Proceedings of the Annual Meeting of the Association of Computational Linguistics (Short Papers)

First Page

605

Last Page

610

Rights Management

Copyright 2014 Association for Computational Linguistics

Abstract or Description

To support empirical study of online privacy policies, as well as tools for users with privacy concerns, we consider the problem of aligning sections of a thousand policy documents, based on the issues they address. We apply an unsupervised HMM; in two new (and reusable) evaluations, we find the approach more effective than clustering and topic models.

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Published In

Proceedings of the Annual Meeting of the Association of Computational Linguistics (Short Papers), 605-610.