Automated Extraction of Regulated Information Types Using Hyponymy Relations

Bhatia, Jaspreet; Evans, Morgan C.; Wadkar, Sudarshan; Breaux, Travis D.

doi:10.1109/rew.2016.018

Cited by 19 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, these privacy laws are notoriously complex and have ambiguous language [52]. Bhatia et al empirically analyzed these privacy policies using Tregex Patterns [53], and Reidenberg et al proposed a natural language processing (NLP) technique to improve the clarity of the policies so companies can use them [54]. Nonetheless, privacy requirements elicited from these laws are not enough to address user concerns [55].…”

Section: Related Work a Privacy Related Requirements Elicitationmentioning

confidence: 99%

Narratives: The Unforeseen Influencer of Privacy Concerns

2022

View full text Add to dashboard Cite

Privacy requirements are increasingly growing in importance as new privacy regulations are enacted. To adequately manage privacy requirements, organizations not only need to comply with privacy regulations, but also consider user privacy concerns. In this exploratory study, we used Reddit as a source to understand users' privacy concerns regarding software applications. We collected 4.5 million posts from Reddit and classified 129075 privacy related posts, which is a non-negligible number of privacy discussions. Next, we clustered these posts and identified 9 main areas of privacy concerns. We use the concept of narratives from economics (i.e., posts that can go viral) to explain the phenomenon of what and when users change in their discussion of privacy. We further found that privacy discussions change over time and privacy regulatory events have a short term impact on such discussions. However, narratives have a notable impact on what and when users discussed about privacy. Considering narratives could guide software organizations in eliciting the relevant privacy concerns before developing them as privacy requirements.

show abstract

Section: Related Work a Privacy Related Requirements Elicitationmentioning

confidence: 99%

Narratives: The Unforeseen Influencer of Privacy Concerns

2022

View full text Add to dashboard Cite

show abstract

“…fectively also requires several additional capabilities such as reasoning over vagueness and ambiguity, understanding elements such as lists (including when they are intended to be exhaustive and when they are not (Bhatia et al, 2016)), effectively incorporating 'co-text'-aspects of web document structure such as document headers that are meaningful semantically to the content of privacy policies (Mysore Gopinath et al, 2018) and incorporating domain knowledge (for example, understanding whether information is sensitive requires background knowledge in the form of applicable regulation). Privacy policies also differ from several closely related domains, such as legal texts which are largely meant to be processed by domain experts.…”

Section: Taskmentioning

confidence: 99%

“…Shvartzshanider et al (2018);Shvartzshnaider et al (2019Shvartzshnaider et al ( , 2020 identify contextual integrity parameters (Nissenbaum, 2004) in policy text. Studies have also tried to extract other, more specific kinds of information from policies, such as third party entities (Libert, 2018b;Bokaie Hosseini et al, 2020) and information about regulated information types (Bhatia et al, 2016;Evans et al, 2017) as well as their similarity (Hosseini et al, 2016). There have also been efforts to analyze vague statements in privacy policies Lebanoff and Liu, 2018), and explore how benchmarks in this domain can be constructed through crowdsourcing Wilson et al, 2016c;Audich et al, 2018).…”

Section: Other Applicationsmentioning

confidence: 99%

Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy?

Ravichander¹,

Black²,

Norton³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

View full text Add to dashboard Cite

Privacy plays a crucial role in preserving democratic ideals and personal autonomy. The dominant legal approach to privacy in many jurisdictions is the "Notice and Choice" paradigm, where privacy policies are the primary instrument used to convey information to users. However, privacy policies are long and complex documents that are difficult for users to read and comprehend. We discuss how language technologies can play an important role in addressing this information gap, reporting on initial progress towards helping three specific categories of stakeholders take advantage of digital privacy policies: consumers, enterprises, and regulators. Our goal is to provide a roadmap for the development and use of language technologies to empower users to reclaim control over their privacy, limit privacy harms, and rally research efforts from the community towards addressing an issue with large social impact. We highlight many remaining opportunities to develop language technologies that are more precise or nuanced in the way in which they use the text of privacy policies.

show abstract

“…In relation to legal requirements specifically, Bhatia et al [9], [39] and Evans et al [40] apply constituency and dependency parsing for analyzing privacy policies. These threads of work have provided us with useful inspiration.…”

Section: Constituency and Dependency Parsing In Rementioning

confidence: 99%

Automated Extraction of Semantic Legal Metadata using Natural Language Processing

Sleimi

Sannier

Sabetzadeh

et al. 2018

2018 IEEE 26th International Requirements Engineering Conference (RE)

View full text Add to dashboard Cite

Context] Semantic legal metadata provides information that helps with understanding and interpreting the meaning of legal provisions. Such metadata is important for the systematic analysis of legal requirements.[Objectives] Our work is motivated by two observations: (1) The existing requirements engineering (RE) literature does not provide a harmonized view on the semantic metadata types that are useful for legal requirements analysis.(2) Automated support for the extraction of semantic legal metadata is scarce, and further does not exploit the full potential of natural language processing (NLP). Our objective is to take steps toward addressing these limitations.[Methods] We review and reconcile the semantic legal metadata types proposed in RE. Subsequently, we conduct a qualitative study aimed at investigating how the identified metadata types can be extracted automatically.[Results and Conclusions] We propose (1) a harmonized conceptual model for the semantic metadata types pertinent to legal requirements analysis, and (2) automated extraction rules for these metadata types based on NLP. We evaluate the extraction rules through a case study. Our results indicate that the rules generate metadata annotations with high accuracy.

show abstract

Automated Extraction of Regulated Information Types Using Hyponymy Relations

Cited by 19 publications

References 12 publications

Narratives: The Unforeseen Influencer of Privacy Concerns

Narratives: The Unforeseen Influencer of Privacy Concerns

Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy?

Automated Extraction of Semantic Legal Metadata using Natural Language Processing

Contact Info

Product

Resources

About