Learning Specific Ontology for Un-Supervised Text Classification

大量文本数据的急剧增长导致了可扩展性和噪声信息方面的挑战越来越多。监督分类已经变得昂贵和耗时，因为获取大量类别的训练集变得更加复杂，并且分类器对数据敏感。无监督分类已经成为一个有吸引力的替代方案，因为它不需要训练集。然而，无监督分类仍然很复杂，并且在概念和特征的理解之间存在差距。本项目旨在利用领域本体来寻找特定的本体，以弥合差距，从而为无监督分类带来突破。它为大文本数据分类提供了基础。

The dramatic rise in massive text data has led to an increasing number of challenges in scalability and noisy information. Supervised classification has become expensive and time consuming as acquiring training sets for a large number of categories becomes more complex and classifiers are sensitive to data. Un-supervised classification has become an attractive alternative given it does not require training sets. However, un-supervised classification is still complex and there is a gap between understanding of concepts and features. This project aims to exploit domain ontology to find specific ontology which can bridge the gap, leading to a breakthrough for un-supervised classification. It provides foundations for classifying big text data.