Please use this identifier to cite or link to this item: https://doi.org/10.1109/IJCNN.2012.6252618
Title: Keyword extraction using backpropagation neural networks and rule extraction
Authors: Azcarraga, A.
Liu, M.D.
Setiono, R. 
Keywords: Backpropagation
document analysis
keyword extraction
rule extraction
Issue Date: 2012
Source: Azcarraga, A.,Liu, M.D.,Setiono, R. (2012). Keyword extraction using backpropagation neural networks and rule extraction. Proceedings of the International Joint Conference on Neural Networks. ScholarBank@NUS Repository. https://doi.org/10.1109/IJCNN.2012.6252618
Abstract: Keyword extraction is vital for Knowledge Management System, Information Retrieval System, and Digital Libraries as well as for general browsing of the web. Keywords are often the basis of document processing methods such as clustering and retrieval since processing all the words in the document can be slow. Common models for automating the process of keyword extraction are usually done by using several statistics-based methods such as Bayesian, K-Nearest Neighbor, and Expectation-Maximization. These models are limited by word-related features that can be used since adding more features will make the models more complex and difficult to comprehend. In this research, a Neural Network, specifically a backpropagation network, will be used in generalizing the relationship of the title and the content of articles in the archive by following word features other than TF-IDF, such as position of word in the sentence, paragraph, or in the entire document, and formats such as heading, and other attributes defined beforehand. In order to explain how the backpropagation network works, a rule extraction method will be used to extract symbolic data from the resulting backpropagation network. The rules extracted can then be transformed into decision trees performing almost as accurate as the network plus the benefit of being in an easily comprehensible format. © 2012 IEEE.
Source Title: Proceedings of the International Joint Conference on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/42638
ISBN: 9781467314909
DOI: 10.1109/IJCNN.2012.6252618
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