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| 1 | 基于双通道融合和BiLSTM-attention的评论文本情感分类算法显示文摘对在线商业评论文本的情感进行挖掘,融合评论文本不同特征为分类器提供更多的信息量,提出了一种新的在线电商情感分类算法。首先,针对传统词嵌入模型无法很好地融合词语情感信息特征的不足,考虑了词嵌入特征和词性特征的多特征融合方法;其次,在两种特征融合方法的基础上采用了双通道和单通道的对比来比较分类的准确性,提出了并行的CNN和BiLSTMAttention双通道神经网络模型;最后,使用真实的京东电商评论数据集对所提模型进行了评估,并且在实验中与不同分类算法进行对比。实验结果表明,新的混合方法具有更好的分类准确率、召回率和F1指标。 | 颜礼蓉 朱小栋 陈曦 | 2021 | 上海理工大学学报2021，43，6： | 3 |
| 2 | A survey of current trends in computational predictions of protein-protein interactions显示文摘Proteomics become an important research area of interests in life science after the completion of the human genome project.This scientific is to study the characteristics of proteins at the large-scale data level,and then gain a holistic and comprehensive understanding of the process of disease occurrence and cell metabolism at the protein level.A key issue in proteomics is how to efficiently analyze the massive amounts of protein data produced by high-throughput technologies.Computational technologies with low-cost and short-cycle are becoming the preferred methods for solving some important problems in post-genome era,such as protein-protein interactions(PPIs).In this review,we focus on computational methods for PPIs detection and show recent advancements in this critical area from multiple aspects.First,we analyze in detail the several challenges for computational methods for predicting PPIs and summarize the available PPIs data sources.Second,we describe the state-of-the-art computational methods recently proposed on this topic.Finally,we discuss some important technologies that can promote the prediction of PPI and the development of computational proteomics. | Yanbin WANG Zhuhong YOU Liping LI Zhanheng CHEN | 2020 | Frontiers of Computer Science2020，14，4： | 0 |
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