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• W2954315061 abstract "Text categorization, or text classification, is one of key tasks for representing the semantic information of documents. Traditional deep leaning models for text categorization are generally time-consuming with large-sized datasets due to slow convergence rate. In this paper, we propose a character-level model for short text classification with a combination of convolutional neural network (CNN), gated recurrent unit (GRU) and highway network (HN), which can capture both the global and the local textual semantics while having a tractable computational complexity. In addition, error minimization extreme learning machine (EM-ELM) is incorporated into the proposed model to improve the classification accuracy further. Extensive experiments show that our approach achieves the state-of-the-art performance when the hybrid model based on EM-ELM is trained using large-sized datasets." @default.
• W2954315061 created "2019-07-12" @default.
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• W2954315061 date "2019-06-30" @default.
• W2954315061 modified "2023-09-27" @default.
• W2954315061 title "Character-Level Hybrid Convolutional and Recurrent Neural Network for Fast Text Categorization" @default.
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• W2954315061 doi "https://doi.org/10.1007/978-3-030-23307-5_12" @default.
• W2954315061 hasPublicationYear "2019" @default.
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