Clustering words is a widely used technique in statistical natural language processing. It requires syntactic, semantic, and contextual features. Especially, semantic clustering is gaining a lot of interest. It consists in grouping a set of words expressing the same idea or sharing the same semantic properties. In this paper, we present a new method to integrate semantic classes in a Statistica...

Document clustering is an unsupervised machine learning method that separates a large subject heterogeneous collection (Corpus) into smaller, more manageable, subject homogeneous collections (clusters). Traditional method of document clustering works around extracting textual features like: terms, sequences, and phrases from documents. These features are independent of each other and do not cat...

With the development of semantic web, more and more ontologies are developed for various purposes. In order to evaluate these ontologies, there have emerged numerous ontology analysis approaches. This paper introduces the classical field theory to ontology area, and puts forward a semantic field model to analyze ontology characteristics from the perspective of concept interaction and hierarchic...

Ontologies currently are a hot topic in the areas of Semantic Web. The current clustering research emphasizes the development of a more efficient clustering method and mainly focuses on term weight calculation without considering the domain knowledge. This paper investigates how ontologies can also be applied to the clustering process. To complement the traditional clustering method, more infor...