Journal article
Hierarchical Text Categorization Using Neural Networks
Information retrieval (Boston), Vol.5(1), pp.87-118
01/2002
DOI: 10.1023/A:1012782908347
Abstract
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 define smaller categorization problems based on a predefined hierarchical structure. The final classifier 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 an optimized version of the traditional Rocchio's algorithm adapted for text categorization, as well as flat neural network classifiers are provided. The results show that the use of the hierarchical structure improves text categorization performance with respect to an equivalent flat model. The optimized Rocchio algorithm achieves a performance comparable with that of the hierarchical neural networks.
Details
- Title: Subtitle
- Hierarchical Text Categorization Using Neural Networks
- Creators
- Miguel Ruiz - School of Library and Information Science The University of Iowa 3087 Main Library Iowa City IA 52242-1420 USAPadmini Srinivasan - School of Library and Information Science The University of Iowa 3087 Main Library Iowa City IA 52242-1420 USA
- Resource Type
- Journal article
- Publication Details
- Information retrieval (Boston), Vol.5(1), pp.87-118
- Publisher
- Kluwer Academic Publishers; Boston
- DOI
- 10.1023/A:1012782908347
- ISSN
- 1386-4564
- eISSN
- 1573-7659
- Language
- English
- Date published
- 01/2002
- Academic Unit
- Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984003007202771
Metrics
19 Record Views