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| Classifying Short-texts with Class Feature Extension |
Yunfei Shao( ),Dongsu Liu |
| School of Economics of Management, Xidian University, Xi’an 710126, China |
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Abstract [Objective] This paper proposes a short text classification method based on category feature extension, aiming to address the issue of sparse content in short texts. [Methods] We used the improved TF-IDF model and LDA topic model to construct the keyword set and topic distribution set, which were all based on category features. Then, we expanded the content and vector representations of short texts. Finally, we classified short texts with the help of convolutional neural network. [Results] The classification precision rate of the proposed method was improved by 3.0%, and the recall rate was improved by 4.1%. [Limitations] Only examined the new method with convolutional neural network. [Conclusions] The proposed method can improve the effectiveness of categorization procedures for short texts.
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Received: 18 December 2018
Published: 23 October 2019
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