Automatic Text Categorization Using Neural Networks
DOI:
https://doi.org/10.7152/acro.v8i1.12728Abstract
This paper presents the results obtained from a series of experiments in automatic text categorization of MEDLINE articles. The main goal ofthis research is to build a counter propagation network and to train it in assigning MeSH phrases based on term frequency of single words from title and abstract. The experiments compare the performance of the counterpropagation network against a backpropagation neural network trained for the same purpose. Results obtained by using a set of 2,344 MEDLINE documents are presented and discussed.Downloads
Published
1997-11-01
Issue
Section
Articles
License
Authors who publish with this journal agree to the following terms:- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).