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In solving these problems, we propose a novel end\u2010to\u2010end model called CRAFL, which is based on the convolutional layer with attention mechanism, recurrent neural networks, and improved focal loss function. First, the residual network (ResNet) extracts phrase semantic representations from word embedding vectors and reduces the dimensionality of the input matrix. Then, the attention mechanism differentiates the focus on the output of ResNet, and the long short\u2010term memory layer learns the features of the sequences. Lastly but most significantly, we apply an improved focal loss function to mitigate the problem of data class imbalance. Our model is compared with other state\u2010of\u2010the\u2010art models on the long discourse dataset, and CRAFL model has proven be more efficient for this task.<\/jats:p>","DOI":"10.1155\/2021\/8845362","type":"journal-article","created":{"date-parts":[[2021,1,8]],"date-time":"2021-01-08T20:40:33Z","timestamp":1610138433000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Text Semantic Classification of Long Discourses Based on Neural Networks with Improved Focal Loss"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6686-8054","authenticated-orcid":false,"given":"Dan","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4142-2045","authenticated-orcid":false,"given":"Jin","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,1,8]]},"reference":[{"volume-title":"Semantic Theory: a Linguistic Perspective","year":"1975","author":"Nilsen D. 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