Title : Hierarchically classifying documents using very few wordsAuthors :
The proliferation of topic hierarchies for text documents has resulted in a need for tools that automatically classify new documents within such hierarchies. One can use existing classiiers by ignoring the hierarchical structure, treating the topics as separate classes. Unfortunately , in the context of text categorization, we are faced with a large number of classes and a huge number of relevant features needed to distinguish between them. Consequently, we are restricted to using only very simple classiiers, both because of computational cost and the tendency of complex models to overrt. We propose an approach that utilizes the hierarchical topic structure to decompose the classiication task into a set of simpler problems, one at each node in the classiication tree. As we show, each of these smaller problems can be solved accurately by focusing only on a very small set of features, those relevant to the task at hand. This set of relevant features varies widely throughout the hierarchy, so that, while the overall relevant feature set may be large, each classiier only examines a small subset. The use of reduced feature sets allows us to utilize more complex (proba-bilistic) models, without encountering the computational and robustness diiculties described above. Multiple submission statement: This paper is currently not under review for another conference or journal, nor will it be submitted elsewhere during ML's review period. Abstract. The proliferation of topic hierarchies for text documents has resulted in a need for tools that automatically classify new documents within such hierarchies. One can use existing classiiers by ignoring the hierarchical structure, treating the topics as separate classes. Unfortunately, in the context of text cat-egorization, we are faced with a large number of classes and a huge number of relevant features needed to distinguish between them. Consequently, we are restricted to using only very simple classiiers, both because of computational cost and the tendency of complex models to overrt. We propose an approach that utilizes the hierarchical topic structure to decompose the classiication task into a set of simpler problems, one at each node in the classiication tree. As we show, each of these smaller problems can be solved accurately by focusing only on a very small set of features, those relevant to the task at hand. This set of relevant features varies widely throughout the hierarchy, so that, while the overall relevant feature set may be large, each classiier only examines a …