1,002 research outputs found
Automatic Detection of Vague Words and Sentences in Privacy Policies
Website privacy policies represent the single most important source of
information for users to gauge how their personal data are collected, used and
shared by companies. However, privacy policies are often vague and people
struggle to understand the content. Their opaqueness poses a significant
challenge to both users and policy regulators. In this paper, we seek to
identify vague content in privacy policies. We construct the first corpus of
human-annotated vague words and sentences and present empirical studies on
automatic vagueness detection. In particular, we investigate context-aware and
context-agnostic models for predicting vague words, and explore
auxiliary-classifier generative adversarial networks for characterizing
sentence vagueness. Our experimental results demonstrate the effectiveness of
proposed approaches. Finally, we provide suggestions for resolving vagueness
and improving the usability of privacy policies.Comment: 10 page
Measuring vagueness and subjectivity in texts: from symbolic to neural VAGO
We present a hybrid approach to the automated measurement of vagueness and
subjectivity in texts. We first introduce the expert system VAGO, we illustrate
it on a small benchmark of fact vs. opinion sentences, and then test it on the
larger French press corpus FreSaDa to confirm the higher prevalence of
subjective markers in satirical vs. regular texts. We then build a neural clone
of VAGO, based on a BERT-like architecture, trained on the symbolic VAGO scores
obtained on FreSaDa. Using explainability tools (LIME), we show the interest of
this neural version for the enrichment of the lexicons of the symbolic version,
and for the production of versions in other languages.Comment: Paper to appear in the Proceedings of the 2023 IEEE International
Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT
How to Raise a Robot - A Case for Neuro-Symbolic AI in Constrained Task Planning for Humanoid Assistive Robots
Humanoid robots will be able to assist humans in their daily life, in particular due to their versatile action capabilities. However, while these robots need a certain degree of autonomy to learn and explore, they also should respect various constraints, for access control and beyond. We explore the novel field of incorporating privacy, security, and access control constraints with robot task planning approaches. We report preliminary results on the classical symbolic approach, deep-learned neural networks, and modern ideas using large language models as knowledge base. From analyzing their trade-offs, we conclude that a hybrid approach is necessary, and thereby present a new use case for the emerging field of neuro-symbolic artificial intelligence
Using machine learning for automated detection of ambiguity in building requirements
The rule interpretation step is yet to be fully automated in the compliance checking process, hindering the automation of compliance checking. Whilst existing research has developed numerous methods for automated interpretation of building requirements, none can identify ambiguous requirements. As part of interpreting ambiguous clauses automatically, this research proposed a supervised machine learning method to detect ambiguity automatically, where the best-performing model achieved recall, precision and accuracy scores of 99.0%, 71.1%, and 78.2%, respectively. This research contributes to the body of knowledge by developing a method for automated detection of ambiguity in building requirements to support automated compliance checking
A Survey on Computational Propaganda Detection
Propaganda campaigns aim at influencing people's mindset with the purpose of
advancing a specific agenda. They exploit the anonymity of the Internet, the
micro-profiling ability of social networks, and the ease of automatically
creating and managing coordinated networks of accounts, to reach millions of
social network users with persuasive messages, specifically targeted to topics
each individual user is sensitive to, and ultimately influencing the outcome on
a targeted issue. In this survey, we review the state of the art on
computational propaganda detection from the perspective of Natural Language
Processing and Network Analysis, arguing about the need for combined efforts
between these communities. We further discuss current challenges and future
research directions.Comment: propaganda detection, disinformation, misinformation, fake news,
media bia
On relational learning and discovery in social networks: a survey
The social networking scene has evolved tremendously over the years. It has grown in relational complexities that extend a vast presence onto popular social media platforms on the internet. With the advance of sentimental computing and social complexity, relationships which were once thought to be simple have now become multi-dimensional and widespread in the online scene. This explosion in the online social scene has attracted much research attention. The main aims of this work revolve around the knowledge discovery and datamining processes of these feature-rich relations. In this paper, we provide a survey of relational learning and discovery through popular social analysis of different structure types which are integral to applications within the emerging field of sentimental and affective computing. It is hoped that this contribution will add to the clarity of how social networks are analyzed with the latest groundbreaking methods and provide certain directions for future improvements
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