Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Prev...Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive semantic feature together by using the weighting mechanism and deep learning models. In the proposed method, we apply Bidirectional Encoder Representations from Transformers (BERT) to generate word vectors on the sentence level automatically, and then obtain the statistical feature, the local semantic feature and the overall semantic feature using Term Frequency-Inverse Document Frequency (TF-IDF) weighting approach, Convolutional Neural Network (CNN) and Bidirectional Gate Recurrent Unit (BiGRU). Then, the fusion feature is accordingly obtained for classification. Experiments are conducted on five popular short text classification datasets and a 5G-enabled IoT social dataset and the results show that our proposed method effectively improves the classification performance.展开更多
针对钢渣体积稳定性差、钢渣-沥青混合料道路过早开裂的问题,采用二氧化硅胶体溶液对钢渣进行浸泡改性处理,通过力学性能测试、扫描电子显微镜(scanning electron microscope,SEM)检测、路用性能测试等方法研究了改性钢渣的物理力学性...针对钢渣体积稳定性差、钢渣-沥青混合料道路过早开裂的问题,采用二氧化硅胶体溶液对钢渣进行浸泡改性处理,通过力学性能测试、扫描电子显微镜(scanning electron microscope,SEM)检测、路用性能测试等方法研究了改性钢渣的物理力学性能、改性钢渣-沥青混合料的性能和钢渣的改性机理,并引入灰靶理论决策方法,综合改性钢渣-沥青混合料的各项性能指标,确定钢渣的最佳改性方案。结果表明:钢渣改性后,物理力学性能明显提高;钢渣的改性浓度越大,沥青混合料的高温性能越佳;延长钢渣的改性时间,沥青混合料的低温抗裂性能提高;且钢渣改性之后,沥青混合料的水稳定性能显著提高。基于灰靶决策理论,最终确定钢渣的最佳改性方案是在改性浓度(溶液质量分数)为3%的溶液下浸泡24 h。展开更多
基金supported in part by the Beijing Natural Science Foundation under grants M21032 and 19L2029in part by the National Natural Science Foundation of China under grants U1836106 and 81961138010in part by the Scientific and Technological Innovation Foundation of Foshan under grants BK21BF001 and BK20BF010.
文摘Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive semantic feature together by using the weighting mechanism and deep learning models. In the proposed method, we apply Bidirectional Encoder Representations from Transformers (BERT) to generate word vectors on the sentence level automatically, and then obtain the statistical feature, the local semantic feature and the overall semantic feature using Term Frequency-Inverse Document Frequency (TF-IDF) weighting approach, Convolutional Neural Network (CNN) and Bidirectional Gate Recurrent Unit (BiGRU). Then, the fusion feature is accordingly obtained for classification. Experiments are conducted on five popular short text classification datasets and a 5G-enabled IoT social dataset and the results show that our proposed method effectively improves the classification performance.
文摘针对钢渣体积稳定性差、钢渣-沥青混合料道路过早开裂的问题,采用二氧化硅胶体溶液对钢渣进行浸泡改性处理,通过力学性能测试、扫描电子显微镜(scanning electron microscope,SEM)检测、路用性能测试等方法研究了改性钢渣的物理力学性能、改性钢渣-沥青混合料的性能和钢渣的改性机理,并引入灰靶理论决策方法,综合改性钢渣-沥青混合料的各项性能指标,确定钢渣的最佳改性方案。结果表明:钢渣改性后,物理力学性能明显提高;钢渣的改性浓度越大,沥青混合料的高温性能越佳;延长钢渣的改性时间,沥青混合料的低温抗裂性能提高;且钢渣改性之后,沥青混合料的水稳定性能显著提高。基于灰靶决策理论,最终确定钢渣的最佳改性方案是在改性浓度(溶液质量分数)为3%的溶液下浸泡24 h。
