{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T18:58:05Z","timestamp":1769713085882,"version":"3.49.0"},"reference-count":13,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,11,4]]},"abstract":"<jats:p>Existing research on Chinese text classification primarily focuses on classifying data information at different granularities, such as character, word, sentence, and chapter. However, this approach often fails to capture the semantic information embedded in these different levels of granularity. To enhance the extraction of the text\u2019s core content, this study proposes a text classification model that incorporates an attention mechanism to fuse multi-granularity information. The model begins by constructing embedding vectors for characters, words, and sentences. Character and word vectors are generated using the Word2Vec training model, allowing the data to be converted into these respective vectors. To capture contextual semantic features, a bidirectional long and short-term memory network is employed for character and word vectors. Sentence vectors, on the other hand, are processed using the FastText model to extract the features they contain. To extract further important semantic information from the different feature vectors, they are fed into an attention mechanism layer. This layer enables the model to prioritize and emphasize the most significant information within the text. Experimental results demonstrate that the proposed model outperforms both single-granularity classification and combinations of two or more granularities. The model exhibits improved classification accuracy across three publicly available Chinese datasets.<\/jats:p>","DOI":"10.3233\/jifs-233388","type":"journal-article","created":{"date-parts":[[2023,8,13]],"date-time":"2023-08-13T15:07:33Z","timestamp":1691939253000},"page":"7631-7645","source":"Crossref","is-referenced-by-count":1,"title":["A multi granularity information fusion text classification model based on attention mechanism"],"prefix":"10.1177","volume":"45","author":[{"given":"Jingfang","family":"Chen","sequence":"first","affiliation":[{"name":"Hunan International Economics University, Changsha, China"},{"name":"Hunan International Economics University, Changsha, China"}]}],"member":"179","reference":[{"issue":"11","key":"10.3233\/JIFS-233388_ref2","first-page":"2256","article-title":"Granular fusion news text topic classification model","volume":"41","author":"Yang","year":"2020","journal-title":"Journal of Chinese Computer Systems"},{"issue":"3","key":"10.3233\/JIFS-233388_ref5","doi-asserted-by":"crossref","first-page":"230","DOI":"10.4304\/jcp.4.3.230-237","article-title":"An improved KNN text classification algorithm based on clustering","volume":"4","author":"Zhou","year":"2009","journal-title":"Journal of Computers"},{"issue":"1","key":"10.3233\/JIFS-233388_ref6","first-page":"71","article-title":"New Na\u00efve Bayes text classification algorithm","volume":"29","author":"Di","year":"2014","journal-title":"Journal of Data Acquisition and Processing"},{"key":"10.3233\/JIFS-233388_ref8","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advantages in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognition"},{"key":"10.3233\/JIFS-233388_ref9","first-page":"64","article-title":"Recurrent neural networks","volume":"5","author":"Medsker","year":"2001","journal-title":"Design and Applications"},{"issue":"8","key":"10.3233\/JIFS-233388_ref10","first-page":"1735","article-title":"Long short term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"European Computation"},{"issue":"18","key":"10.3233\/JIFS-233388_ref11","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","article-title":"Framewise phone classification with bidirectional LSTM and other neural network architectures","author":"Graves","year":"2005","journal-title":"European Networks"},{"issue":"1","key":"10.3233\/JIFS-233388_ref14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s44196-021-00055-4","article-title":"Research on multi label text classification method based on tALBERT \u2013CNN","volume":"14","author":"Liu","year":"2021","journal-title":"International Journal of Computational Intelligence Systems"},{"issue":"1","key":"10.3233\/JIFS-233388_ref15","first-page":"40","article-title":"Question classification based on MAC-LSTM","volume":"37","author":"Yu","year":"2020","journal-title":"Application Research of Comptuers"},{"issue":"4","key":"10.3233\/JIFS-233388_ref16","first-page":"86","article-title":"New method of text representation model based on neural network","volume":"38","author":"Zeng","year":"2017","journal-title":"Journal of Communications"},{"issue":"3","key":"10.3233\/JIFS-233388_ref18","first-page":"40","article-title":"Research on network news text classification model based on fasttext and attention mechanism","volume":"42","author":"Wang","year":"2022","journal-title":"Journal of Modern Information"},{"issue":"3","key":"10.3233\/JIFS-233388_ref21","first-page":"172","article-title":"BiLSTM_CNN classification model based on self attention and residual network","volume":"58","author":"Yang","year":"2022","journal-title":"Computer Engineering and Applications"},{"key":"10.3233\/JIFS-233388_ref22","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.neucom.2021.02.069","article-title":"A hybrid medical text classification framework: Integrating attention rule construction and neural network","volume":"443","author":"Li","year":"2021","journal-title":"Eurocomputing"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-233388","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T09:25:49Z","timestamp":1769678749000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-233388"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,4]]},"references-count":13,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3233\/jifs-233388","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,4]]}}}