CINXE.COM

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <title>ComSIS | Computer Science and Information Systems</title> <link rel="stylesheet" type="text/css" href="res/style1.css" /> </head> <body> <script type="text/javascript" src="res/wz_tooltip.js"></script> <script type="text/javascript" src="res/slide.js"></script> <div id="all"> <div id="header"> <h1>Computer Science and Information Systems</h1> </div>  <div id="main"> <div id="sidebar"> <p>About the journal</p> <ul> <li><a href="index.php">Home page</a></li> <li><a href="contact.php">Contact information</a></li> <li><a href="aims.php">Aims and scope</a></li> <li><a href="indexing.php">Indexing information</a></li> <li><a href="policies.php">Editorial policies</a></li> <li><a href="consortium.php">ComSIS consortium</a></li> <li><a href="boards.php">Journal boards</a></li> <li><a href="managing.php">Managing board</a></li> </ul> <p>For authors</p> <ul> <li><a href="information.php">Information for contributors</a></li> <li><a href="http://ojs.pmf.uns.ac.rs/index.php/comsis">Paper submission</a></li> <li><a href="submission.php">Article submission through OJS</a></li> <li><a href="copyright.php">Copyright transfer form</a></li> <li><a href="download.php">Download section</a></li> </ul> <p>For readers</p> <ul> <li><a href="archive.php?show=lstnew">Forthcoming articles</a></li> <li><a href="archive.php?show=vol2104">Current issue</a></li> <li><a href="archive.php">Archive</a></li> </ul> <p>For reviewers</p> <ul> <li><a href="http://ojs.pmf.uns.ac.rs/index.php/comsis">View and review submissions</a></li> </ul> <p>News</p> <ul> <li><a href="https://www.facebook.com/ComSISJournal/"> <img src="res/fb.png" alt="FB"/> Journal's Facebook page</a></li> <li><a href="cfp.php">Calls for special issues</a></li> <li><a href="notification.php">New issue notification</a></li> </ul> </div>  <div id="content">  <h1 class="title">A multi-feature Fusion Model Based on Long and Short Term Memory Network and Improved Artificial Bee Colony Algorithm for English Text Classification</h1><p class="authors">Tianying Wen<sup>1</sup></p><ol><li>Department of Education, Liaoning National Normal College<br/>No. 45, Chongdong Road, Huanggu District, Shenyang, 110032, China<br/>sarkozyteague@foxmail.com</li></ol><h3>Abstract</h3><p>The traditional methods of English text classification have two disadvantages. One is that they cannot fully represent the semantic information of the text. The other is that they cannot fully extract and integrate the global and local information of the text. Therefore, we propose a multi-feature fusion model based on long and short term memory network and improved artificial bee colony algorithm for English text classification. In this method, the character-level vector and word-level vector representations of English text are calculated using a pre-training model to obtain a more comprehensive text feature vector representation. Then the multi-head attention mechanism is used to capture the dependencies in the text sequence to improve the semantic understanding of the text. Through feature fusion, the channel features are optimized and the spatial features and time series features are combined to improve the classification performance of the hybrid model. In the stage of network training, the weighted linear combination of maximum Shannon entropy and minimum cross entropy is used as the return degree evaluation function of the bee colony algorithm, and the scale factor is introduced to adjust the solution search strategy of leading bees and following bees, and the improved artificial bee colony algorithm is combined with the classification network to realize the automatic optimization and adjustment of network parameters. Experiments are carried out on public data set. Compared with traditional convolutional neural networks, the classification accuracy of the new model increases by 2% on average, and the accuracy of data set increases by 2.4% at the highest.</p><h3>Key words</h3><p>English text classification, multi-feature fusion, artificial bee algorithm, long and short term memory network, multi-head attention mechanism</p><h3>Digital Object Identifier (DOI)</h3><p><a href="https://doi.org/10.2298/CSIS240314050W">https://doi.org/10.2298/CSIS240314050W</a></p><h3>Publication information</h3><p><a href="/archive.php?show=vol2104">Volume 21, Issue 4 (September 2024)</a><br/>Year of Publication: 2024<br/>ISSN: 2406-1018 (Online)<br/>Publisher: ComSIS Consortium</p><h3>Full text</h3><p><a class="download" href="pdf.php?id=17111"><img class="left" src="res/pdf.png" alt="Download"/>Available in PDF<br/><em>Portable Document Format</em></a></p><h3>How to cite</h3><p>Wen, T.: A multi-feature Fusion Model Based on Long and Short Term Memory Network and Improved Artificial Bee Colony Algorithm for English Text Classification. Computer Science and Information Systems, Vol. 21, No. 4, 1607–1627. (2024), https://doi.org/10.2298/CSIS240314050W</p>  </div>  </div>  <div id="footer_top"> </div> <div id="footer"> <div class="left">Faculty of Sciences, Trg Dositeja Obradovića 3, 21000 Novi Sad, Serbia, <a href="mailto:comsis@uns.ac.rs">comsis@uns.ac.rs</a></div> <div class="left">Published by ComSIS Consortium under<br/><a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License<br><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png"/></a></div> <div class="clearer"> </div> </div>  </div>  </body> </html>