Hierarchical Neural Networks for Text Categorization.
Miguel E. Ruiz, Parthasrathy Srinivasan · 1999
This paper presents the design and evaluation of a text categorization method based on the Hierarchical Mixture of Experts model. This model uses a divide and conquer principle to dene smaller categorization problems based on a predened hierarchical structure. The nal classier is a hierarchical array of neural networks. The method is evaluated using the UMLS Metathesaurus as the underlying hierarchical structure, and the OHSUMED test set of MEDLINE records. Comparisons with traditional Rocchio's algorithm adapted for text categorization, as well as at neural network classi- ers are provided. The results show that the use of the hierarchical structure improves text categorization performance signicantly. 1 Introduction Text categorization, also known as automatic indexing, is the process of algorithmically analyzing an electronic document to assign a set of categories (or index terms) that succinctly describe the content of the document. This assignment can be used for classic...