Traditional text learning algorithms need labeled documents to supervise the learning process, but labeling documents of a specific class is often expensive and time consuming. We observe it is convenient to use some keywords(i.e. class-descriptions) to describe class sometimes. However, short class-description usually does not contain enough information to guide classification. Fortunately, large amount of public data is easily acquired, i.e. ODP, Wikipedia and so on, which contains enormous knowledge. In this paper, we address the text classification problem with such knowledge rather than any labeled documents and propose a co-clustering based knowledge-supervised learning algorithm (CoCKSL) in information theoretic framework, which effectively applies the knowledge to classification tasks.

Most classification algorithms are best at categorizing the Web documents into a few categories, such as the top two levels in the Open Directory Project. Such a classification method does not give very detailed topic-related class information for the user because the first two levels are often too coarse. However, classification on a large-scale hierarchy is known to be intractable for many target categories with cross-link relationships among them. In this paper, we propose a novel deep-classification approach to categorize Web documents into categories in a large-scale taxonomy. The approach consists of two stages: a search stage and a classification stage. In the first stage, a category-search algorithm is used to acquire the category candidates for a given document. Based on the category candidates, we prune the large-scale hierarchy to focus our classification effort on a small subset of the original hierarchy. As a result, the classification model is trained on the small subset before being applied to assign the category for a new document. Since the category candidates are sufficiently close to each other in the hierarchy, a statistical-language-model based classifier using n-gram features is exploited. Furthermore, the structure of the taxonomy can be utilized in this stage to improve the performance of classification. We demonstrate the performance of our proposed algorithms on the Open Directory Project with over 130,000 categories. Experimental results show that our proposed approach can reach 51.8% on the measure of Mi-F1 at the 5th level, which is 77.7% improvement over top-down based SVM classification algorithms.

Organizing Web search results into hierarchical categories facilitates users' browsing through Web search results, especially for ambiguous queries where the potential results are mixed together. Previous methods on search result classification are usually based on pre-training a classification model on some fixed and shallow hierarchical categories, where only the top-two-level categories of a Web taxonomy is used. Such classification methods may be too coarse for users to browse, since most search results would be classified into only two or three shallow categories. Instead, a deep hierarchical classifier must provide many more categories. However, the performance of such classifiers is usually limited because their classification effectiveness can deteriorate rapidly at the third or fourth level of a hierarchy. In this paper, we propose a novel algorithm known as Deep Classifier to classify the search results into detailed hierarchical categories with higher effectiveness than previous approaches. Given the search results in response to a query, the algorithm first prunes a wide-ranged hierarchy into a narrow one with the help of some Web directories. Different strategies are proposed to select the training data by utilizing the hierarchical structures. Finally, a discriminative naíve Bayesian classifier is developed to perform efficient and effective classification. As a result, the algorithm can provide more meaningful and specific class labels for search result browsing than shallow style of classification. We conduct experiments to show that the Deep Classifier can achieve significant improvement over state-of-the-art algorithms. In addition, with sufficient off-line preparation, the efficiency of the proposed algorithm is suitable for online application