Learning Specific Ontology for Un-Supervised Text Classification

学习无监督文本分类的特定本体

• 批准号: DP140103157
• 负责人: Prof Yuefeng Li
• 金额: $ 20.97万
• 依托单位: Queensland University of Technology
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2014
• 资助国家: 澳大利亚
• 起止时间: 2014-10-15 至 2020-06-30
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP140103157
• 关键词: Learning; Specific; Ontology; Un; Supervised

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.

海量文本数据的急剧增长导致可扩展性和噪声信息方面面临越来越多的挑战。由于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，监督分类变得昂贵且耗时。由于不需要训练集，无监督分类已成为一种有吸引力的替代方案。然而，无监督分类仍然很复杂，对概念和特征的理解之间存在差距。该项目旨在利用领域本体来找到可以弥补差距的特定本体，从而实现无监督分类的突破。它为大文本数据的分类提供了基础。