{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,4]],"date-time":"2026-01-04T21:05:33Z","timestamp":1767560733536},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684802","type":"print"},{"value":"9781643684819","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,12]]},"abstract":"<jats:p>Most existing convolutional neural network-based text classification methods have problems such as inadequate feature extraction, long-distance dependency. This study presents a novel deep multi-feature fusion neural network for text classification. The convolutional neural network is employed to extract the local features of the text. An improved encoder with self-attention method is proposed to build the global relationships. Furthermore, a multiple feature fusion strategy is provided to integrate the features learned from the CNN and the encoder, which is beneficial to excavating the potential semantic information in a global perspective. The experimental results demonstrate that our proposed method has better classification performance than the alternative approaches.<\/jats:p>","DOI":"10.3233\/faia231239","type":"book-chapter","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:56:45Z","timestamp":1705064205000},"source":"Crossref","is-referenced-by-count":1,"title":["Deep Multi-Feature Fusion Neural Network for Text Classification"],"prefix":"10.3233","author":[{"given":"Jingjing","family":"Yang","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science & Technology University, Beijing, 100192, China"},{"name":"School of Automation, Beijing Information Science & Technology University, Beijing, 100192, China"}]},{"given":"Feng","family":"Deng","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science & Technology University, Beijing, 100192, China"},{"name":"School of Automation, Beijing Information Science & Technology University, Beijing, 100192, China"}]},{"given":"Shiqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science & Technology University, Beijing, 100192, China"},{"name":"School of Automation, Beijing Information Science & Technology University, Beijing, 100192, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Electronics, Communications and Networks"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA231239","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:56:46Z","timestamp":1705064206000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA231239"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"ISBN":["9781643684802","9781643684819"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia231239","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,12]]}}}