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Combining Classifiers for Word Sense Disambiguation Based on DempsterShafer Theory and OWA Operators Abstract
"... In this paper, we discuss a framework for weighted combination of classifiers for word sense disambiguation (WSD). This framework is essentially based on DempsterShafer theory of evidence (Dempster, 1967; Shafer, 1976) and ordered weighted averaging (OWA) operators (Yager, 1988). We first determine ..."
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Cited by 3 (1 self)
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In this paper, we discuss a framework for weighted combination of classifiers for word sense disambiguation (WSD). This framework is essentially based on DempsterShafer theory of evidence (Dempster, 1967; Shafer, 1976) and ordered weighted averaging (OWA) operators (Yager, 1988). We first
Qualitative DempsterShafer Theory
, 1993
"... This paper introduces the idea of using the DempsterShafer theory of evidence with qualitative values. DempsterShafer theory is a formalism for reasoning under uncertainty which may be viewed as a generalisation of probability theory with special advantages in its treatment of ambiguous data and t ..."
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Cited by 6 (3 self)
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This paper introduces the idea of using the DempsterShafer theory of evidence with qualitative values. DempsterShafer theory is a formalism for reasoning under uncertainty which may be viewed as a generalisation of probability theory with special advantages in its treatment of ambiguous data
Combination of evidence in DempsterShafer theory
, 2002
"... DempsterShafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals. DempsterShafer theory does not require a ..."
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Cited by 79 (2 self)
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expert elicitation. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them. This report surveys a number of possible combination rules for DempsterShafer structures and provides examples of the implementation
DempsterShafer Argument Schemes
"... Abstract. DempsterShafer theory, which can be regarded as a generalisation of probability theory, is a widely used formalism for reasoning with uncertain information. The application of the theory hinges on the use of a rule for combining evidence from different sources. A number of different comb ..."
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Abstract. DempsterShafer theory, which can be regarded as a generalisation of probability theory, is a widely used formalism for reasoning with uncertain information. The application of the theory hinges on the use of a rule for combining evidence from different sources. A number of different
DempsterShafer for Anomaly Detection
"... Abstract—In this paper, we implement an anomaly detection system using the DempsterShafer method. Using two standard benchmark problems we show that by combining multiple signals it is possible to achieve better results than by using a single signal. We further show that by applying this approach t ..."
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Abstract—In this paper, we implement an anomaly detection system using the DempsterShafer method. Using two standard benchmark problems we show that by combining multiple signals it is possible to achieve better results than by using a single signal. We further show that by applying this approach
Automatic Word Sense Discrimination
 Journal of Computational Linguistics
, 1998
"... This paper presents contextgroup discrimination, a disambiguation algorithm based on clustering. Senses are interpreted as groups (or clusters) of similar contexts of the ambiguous word. Words, contexts, and senses are represented in Word Space, a highdimensional, realvalued space in which closen ..."
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Cited by 530 (1 self)
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This paper presents contextgroup discrimination, a disambiguation algorithm based on clustering. Senses are interpreted as groups (or clusters) of similar contexts of the ambiguous word. Words, contexts, and senses are represented in Word Space, a highdimensional, realvalued space in which
Algorithms for dempstershafer theory
 Algorithms for Uncertainty and Defeasible Reasoning
, 2000
"... The method of reasoning with uncertain information known as DempsterShafer theory arose from the reinterpretation and development of work of Arthur Dempster [Dempster, 67; 68] by Glenn Shafer in his book a mathematical theory of evidence [Shafer, 76], and further publications e.g., [Shafer, 81; 90] ..."
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Cited by 20 (3 self)
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The method of reasoning with uncertain information known as DempsterShafer theory arose from the reinterpretation and development of work of Arthur Dempster [Dempster, 67; 68] by Glenn Shafer in his book a mathematical theory of evidence [Shafer, 76], and further publications e.g., [Shafer, 81; 90
The DempsterShafer calculus for statisticians
 International Journal of Approximate Reasoning
, 2007
"... The DempsterShafer (DS) theory of probabilistic reasoning is presented in terms of a semantics whereby every meaningful formal assertion is associated with a triple (p, q, r) where p is the probability “for ” the assertion, q is the probability “against” the assertion, and r is the probability of “ ..."
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Cited by 46 (1 self)
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The DempsterShafer (DS) theory of probabilistic reasoning is presented in terms of a semantics whereby every meaningful formal assertion is associated with a triple (p, q, r) where p is the probability “for ” the assertion, q is the probability “against” the assertion, and r is the probability
Bayesian Network Classifiers
, 1997
"... Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with stateoftheart classifiers such as C4.5. This fact raises the question of whether a classifier with less restr ..."
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Cited by 788 (23 self)
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restrictive assumptions can perform even better. In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks. These networks are factored representations of probability distributions that generalize the naive Bayesian classifier and explicitly
Results 1  10
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