Really Vague? Automatically Identify the Potential False Vagueness within the Context of Documents

Xiaoli Lian, Dan Huang, Xuefeng Li, Ziyan Zhao (), Zhiqiang Fan and Min Li
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Xiaoli Lian: School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Dan Huang: School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Xuefeng Li: School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Ziyan Zhao: School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Zhiqiang Fan: North China Institute of Computing Technology, Beijing 100083, China
Min Li: North China Institute of Computing Technology, Beijing 100083, China

Mathematics, 2023, vol. 11, issue 10, 1-22

Abstract: Privacy policies are critical for helping individuals make decisions on the usage of information systems. However, as a common language phenomenon, ambiguity occurs pervasively in privacy policies and largely impedes their usefulness. The existing research focuses on the identification of individual vague words or sentences, without considering the context of documents, which may cause a significant amount of false vagueness. Our goal is to automatically detect the potential false vagueness and the related supporting evidence, which illustrates or explains the vagueness, and therefore probably assist in alleviating the vagueness. We firstly analyze the public manual annotations and define four common patterns of false vagueness and three types of supporting evidence. Then we propose the approach of the F·vague-Detector to automatically detect the supporting evidence and then locate the corresponding potential false vagueness. According to our analysis, about 29–39% of individual vague sentences have at least one clarifying sentence in the documents, and experiments show good performance of our approach, with recall of 66.98–67.95%, precision of 70.59–94.85%, and F 1 of 69.24–78.51% on the potential false vagueness detection. Detecting the vagueness of isolated sentences without considering their context within the whole document would bring about one-third potential false vagueness, and our approach can detect this potential false vagueness and the alleviating evidence effectively.

Keywords: privacy policy; potential false vagueness; NLP; vagueness (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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