Text categorization methods application for natural language call routing

Natural language call routing can be treated as an instance of topic categorization of documents after speech recognition of calls. This categorization consists of two important parts. The first one is text preprocessing for numerical data extraction and the second one is classification with machine learning methods. This paper focuses on different text preprocessing methods applied for call routing. Different machine learning algorithms with several text representations have been applied for this problem. A novel text preprocessing technique has been applied and investigated. Numerical experiments have shown computational and classification effectiveness of the proposed method in comparison with standard techniques. Also a novel features selection method was proposed. The novel features selection method has demonstrated some advantages in comparison with standard techniques.