We present an approach to model hidden attributes in the compositional semantics of adjective-noun phrases in a distributional model. For the representation of adjective meanings, we reformulate the pattern-based approach for attribute learning of Almuhareb (2006) in a structured vector space model (VSM). This model is complemented by a structured vector space representing attribute dimensions ...

Attribute grammars (AGs) are known to be a useful formalism for semantic analysis and translation. However, debugging AGs is complex owing to inherent difficulties of AGs, such as recursive grammar structure and attribute dependency. In this paper, a new systematic method of debugging AGs is proposed. Our approach is, in principle, based on previously proposed algorithmic debugging of AGs, but ...

Attribute grammars (AGs) are known to be a useful formalism for semantic analysis and translation. However, debugging AGs is complex owing to inherent difficulties of AGs, such as recursive grammar structure and attribute dependency. In this paper, a new systematic method of debugging AGs is proposed. Our approach is, in principle, based on previously proposed algorithmic debugging of AGs, but ...

Abstract: Enterprise architecture (EA) frameworks are used to ensure interoperability of information systems and improve the effectiveness and efficiency of business organisations. Several methods have been proposed for selecting suitable frameworks. Although these methods are useful, none of them captures the uncertainties inherent in multi-attribute framework selection problems that embrace b...

As one of the key topics in development neighborhood rough set, attribute reduction has attracted extensive attentions because its practicability and interpretability for dimension or feature selection. Although random sampling strategy been introduced to avoid overfitting, uncontrollable may still affect efficiency search reduct. By utilizing inherent characteristics each label, Multi-label le...

Clustering categorical data is more complicated than the numerical clustering because of its special properties. Scalability and memory constraint is the challenging problem in clustering large data set. This paper presents an incremental algorithm to cluster the categorical data. Frequencies of attribute values contribute much in clustering similar categorical objects. In this paper we propose...

using current employee performance data to predict the future behavior of the applicants is an interesting area which can broaden new horizons of knowledge lay in the organization. because of inherent ambiguity and uncertainty, cognitive limitations of the human mind make unknown behaviors of very complex systems difficult to predict. as a consequence, it is necessary to model the imprecise mod...

In this paper a new and original name evaluation method of attribute names is proposed and elaborated. This method allows an attribute name to be presented in a non-hierarchy fashion and to be evaluated using a hierarchical approach. A three-level name model that supports attribute names for objects in a distributed system and the structure of naming contexts which bind attribute names onto obj...