Full-text documents are a vital and rapidly growing part of online biomedical information. A single large document can contain as much information as a small database, but normally lacks the tight structure and consistent indexing of a database. Retrieval systems will often miss highly relevant part
Hybrid hierarchical classifiers for categorization of medical documents
β Scribed by Miguel E. Ruiz; Padmini Srinivasan
- Publisher
- Wiley (John Wiley & Sons)
- Year
- 2005
- Tongue
- English
- Weight
- 507 KB
- Volume
- 40
- Category
- Article
- ISSN
- 0044-7870
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β¦ Synopsis
Abstract
This article presents a study of the application of hierarchical classifiers based on the hierarchical mixtures of experts. In particular we present an extension of our work that explores the use of linear classifiers and a hybrid model that combines backpropagation neural networks with linear classifiers. We test this model using the UMLS as the classification structure and a subset of medical abstracts from MEDLINE. Our results confirm that using the hierarchical structure of the classification vocabulary improves categorization performance.
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