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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Taeho Jo Lee, M. Gatton, T.M. |
| Copyright Year | 2006 |
| Description | Author affiliation: National University, 11255 North Torrey Pines Road (Gatton, T.M.) || University of Ottawa, 800 King Edward (Taeho Jo) || ChonBuk National University (Lee, M.) |
| Abstract | A document surrogate is usually represented in a list of words. Because not all words in a document reflect its content, it is necessary to select important words from the document that relate to its content. Such important words are called keywords and are selected with a particular equation based on Term Frequency (TF) and Inverted Document Frequency (IDF). Additionally, the position of each word in the document and the inclusion of the word in the title should be considered to select keywords among words contained in the text. The equation based on these factors gets too complicated to be applied to the selection of keywords. This paper proposes a neural network back propagation model in which these factors are used as the features and feature vectors are generated to select keywords. This paper will show that the proposed neural network backpropagation approach outperforms the equation in distinguishing keywords. |
| Sponsorship | IEEE Comput. Soc |
| Starting Page | 194 |
| Ending Page | 197 |
| File Size | 178225 |
| Page Count | 4 |
| File Format | |
| ISBN | 0769526748 |
| DOI | 10.1109/ICHIT.2006.253612 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-11-09 |
| Publisher Place | South Korea |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Text mining Neural networks Natural languages Text categorization keyword extraction Information retrieval Frequency neural networks. Data mining Information technology Equations Indexing |
| Content Type | Text |
| Resource Type | Article |
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