关键词: 短文本分类, 概念描述, 数据挖掘, 机器学习, 自然语言处理

Abstract: In order to solve the problem that traditional classification is not very satisfactory due to fewer text features in short text, an algorithm using concept description was presented. At first, a global semantic concept word list was built. Then the test set and training set were conceptualized by the global semantic concept word list to combine the test short texts by the same description of concept in the training set, and at the same time, training long texts were combined by the training short texts in the training set. At last, the long text was classified by traditional classification algorithm. The experiments show that the proposed method could mine implicit semantic information in short text efficiently while expanding short text on semantics adequately, and improving the accuracy of short text classification.

Key words: Short text classification, Description of concept, Data mining, Machine learning, Natural language processing