In this thesis, we describe design and implementation of a word clustering system using a definition of an entry word in a dictionary, called a dictionary definition. Generally word clustering needs various features like words and performance of a system for the word clustering depends on using some kinds of features. A dictionary definition describes the meaning of an entry in detail, but words in the dictionary definition are implicative or abstractive, and then its length is not long. The word clustering using only features extracted from the dictionary definition results in a lots of small-size clusters. In order to make large-size clusters or improve the performance, we need to transform the features into more general words with keeping the original meaning of the dictionary definition as intact as possible. In this thesis, we propose two methods for extending the dictionary definition using ontology. One is to extend the dictionary definition to parent words on the ontology and the other is to extend the dictionary definition to some words in fixed depth from the root of the ontology. Through our experiments, we have observed that the proposed systems outperform that without extending features, and the latter’s extending method overtakes the former’s extending method in performance. We have also observed that verbs are very useful in extending features in the case of word clustering.