Rough sets: probabilistic versus deterministic approach
Machine learning and uncertain reasoning
Variable precision rough set model
Journal of Computer and System Sciences
Communications of the ACM
&agr;-RST: a generalization of rough set theory
Information Sciences—Informatics and Computer Science: An International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
A Fuzzy Analysis of Linguistic Negation of Nuanced Property in Knowledge-Based Systems
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
An Intelligent System Dealing with Negative Information
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
ACL '81 Proceedings of the 19th annual meeting on Association for Computational Linguistics
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Linguistic negation processing is a challenging problem studied by a large number of researchers from different communities, i.e. logic, linguistics, etc. We are interested in finding the positive interpretations of a negative sentence represented as "x is not A". In this paper, we do not focus on the single set of translations but on two approximation sets. The first one called pessimistic corresponds to the positive translations of the negative sentence that we can consider as sure. The second one called optimistic contains all the sentences that can be viewed as possible translations of the negative sentence. These approximation sets are computed according to the rough sets framework and based on a neighbourhood relation defined on the space of properties. Finally, we apply an original strategy of choice upon the two approximation sets which allows us to select the suitable translations of the initial negative sentence. It appears that we obtain results in good accordance with the ones linguistically expected.