Research Article
Encoding Dual Semantic Knowledge for Text-Enhanced Cloud Services
@INPROCEEDINGS{10.1007/978-3-030-69992-5_12, author={Shicheng Cui and Qianmu Li and Shu-Ching Chen and Jun Hou and Hanrui Zhang and Shunmei Meng}, title={Encoding Dual Semantic Knowledge for Text-Enhanced Cloud Services}, proceedings={Cloud Computing. 10th EAI International Conference, CloudComp 2020, Qufu, China, December 11-12, 2020, Proceedings}, proceedings_a={CLOUDCOMP}, year={2021}, month={2}, keywords={Text classification Dual Semantic Embedding Convolutional Neural Networks}, doi={10.1007/978-3-030-69992-5_12} }- Shicheng Cui
Qianmu Li
Shu-Ching Chen
Jun Hou
Hanrui Zhang
Shunmei Meng
Year: 2021
Encoding Dual Semantic Knowledge for Text-Enhanced Cloud Services
CLOUDCOMP
Springer
DOI: 10.1007/978-3-030-69992-5_12
Abstract
Topic modeling techniques have been widely applied in many cloud computing applications. However, few of them have tried to discover latent semantic relationships of implicit topics and explicit words to generate a more comprehensive representation for each text. To fully exploit the semantic knowledge for text classification in cloud computing systems, we attempt to encode topic and word features based on their latent relationships. The extracted topical information reorganizes the original textual structures from two aspects: one is that the topic extracted by Latent Dirichlet Allocation (LDA) is viewed as a textual extension; the other is that the topic feature performs as a counterpart modality to the word. This paper proposes a Dual Semantic Embedding (DSE) method, which uses Convolutional Neural Networks (CNNs) to encode the dual semantic features of topics and words from the reorganized semantic structures. Experimental results show that DSE improves the performance of text classification and outperforms the state-of-the-art feature generation baselines on micro-(F1)and macro-(F1)scores over the real-world text classification datasets.
