Category Levels in Hierarchical Text Categorization

We consider the problem of assigning level numbers (weights) to hierarchically organized categories during the process of text categorization. These levels control the ability of the categories to attract documents during the categorization process. The levels are adjusted in order to obtain a balance between recall and precision for each category. If a category's recall exceeds its precision, the category is too strong and its level is reduced. Conversely, a category's level is increased to strengthen it if its precision exceeds its recall. The categorization algorithm used is a supervised learning procedure that uses a linear classifier based on the category levels. We are given a set of categories, organized hierarchically. We are also given a training corpus of documents already placed in one or more categories. From these, we extract vocabulary, words that appear with high frequency within a given category, characterizing each subject area. Each node's vocabulary is filtered and its words assigned weights with respect to the specific category. Then, test documents are scanned and categories ranked based on the presence of vocabulary terms. Documents are assigned to categories based on these rankings. We demonstrate that precision and recall can be significantly improved by solving the categorization problem taking hierarchy into account. Specifically, we show that by adjusting the category levels in a principled way, that precision can be significantly improved, from 84% to 91%, on the much-studied Reuters-21578 corpus organized in a three-level hierarchy of categories.