Multi-Dimensional Text Classification

Multi-Dimensional Text Classification Thanaruk THEERAMUNKONG IT Program, SIIT, Thammasat University P.O. Box 22 Thammasat Rangsit Post Office, Pathumthani, Thailand, 12121 ping@siit.tu.ac.th Verayuth LERTNATTEE IT Program, SIIT, Thammasat University P.O. Box 22 Thammasat Rangsit Post Office, Pathumthani, Thailand, 12121 verayuth@siit.tu.ac.th Abstract This paper proposes a multi-dimensional framework for classifying text documents. In this framework, the concept of multi- dimensional category model is introduced for representing classes. In contrast with traditional flat and hierarchical category models, the multi-dimensional category model classifies each text document in a collection using multiple predefined sets of categories, where each set corresponds to a dimension. Since a multi-dimensional model can be converted to flat and hierarchical models, three classification strategies are possible, i.e., classifying directly based on the multi-dimensional model and classifying with the equivalent flat or hierarchical models. The efficiency of these three classifications is investigated on two data sets. Using k-NN, naïve Bayes and centroid- based classifiers, the experimental results show that the multi-dimensional-based and hierarchical-based classification performs better than the flat-based classifications. 1 Introduction In the past, most of previous works on text classification focus on classifying text documents into a set of flat categories. The task is to classify documents into a predefined set of categories (or classes) (Lewis and Ringuetee, 1994; Eui-Hong and Karypis, 2000) where there are no structural relationships among these categories. Many existing databases are organized in this type of flat structure, such as Reuters newswire, OHSUMED and TREC. To improve classification accuracy, a variety of learning techniques are developed, including regression models (Yang and Chute, 1992), nearest neighbour classification (Yang and Liu, 1999), Bayesian approaches (Lewis and Ringuetee, 1994; McCallum et al., 1998), decision trees (Lewis and Ringuetee 1994), neural networks (Wiener et al.,1995) and support vector machines (Dumais and Chen, 2000). However, it is very difficult to browse or search documents in flat categories when there are a large number of categories. As a more efficient method, one possible natural extension to flat categories is to arrange documents in topic hierarchy instead of a simple flat structure. When people organize extensive data sets into fine-grained classes, topic hierarchy is often employed to make the large collection of classes (categories) more manageable. This structure is known as category hierarchy. Many popular search engines and text databases apply this structure, such as Yahoo, Google Directory, Netscape search and MEDLINE. There are many recent works attempting to automate text classification based on this category hierarchy (McCallum et al., 1998; Chuang W. T. et al., 2000). However, with a large number of classes or a large hierarchy, the problem of sparse training data per class at the lower levels in the hierarchy raises and results in decreasing classification accuracy of lower classes. As another problem, the traditional category hierarchy may be too rigid for us to construct since there exist several possible category hierarchies for a data set. To cope with these problems, this paper proposes a new framework, called multi- dimensional framework, for text classification. The framework allows multiple pre-defined …

Content truncated to 500 words