结合GloVe和GRU的文本分类模型

文本分类有着广泛的应用,对其分类算法的研究也一直备受关注。但是,传统文本分类算法普遍存在文本特征向量化维度过高、没有考虑关键词之间语义关系、训练参数过多等问题,这些都将影响到分类准确率等性能。针对这些问题,提出了一种结合词向量化与GRU的文本分类算法。对文本进行预处理操作;通过GloVe进行词向量化,尽可能多地蕴含文本语义和语法信息,同时降低向量空间维度;再利用GRU神经网络模型进行训练,最大程度保留长文本中长距离词之间的语义关联。实验结果证明,该算法对提高文本分类性能有较明显的作用。     

Image-text interaction graph neural network for image-text sentiment analysis

As various social platforms are experiencing fast development, the volume of image-text content generated by users has grown rapidly. Image-text based sentiment of social media analysis has also attracted great interest from researchers in recent years. The main challenge of image-text sentiment analysis is how to construct a model that can promote the complementarity between image and text. In most previous studies, images and text were simply merged, while the interaction between them was not fully considered. This paper proposes an image-text interaction graph neural network for image-text sentiment analysis. A text-level graph neural network is used to extract the text features, and a pre-trained convolutional neural network is employed to extract the image features. Then, an image-text interaction graph network is constructed. The node features of the graph network are initialized by the text features and the image features, while the node features in the graph are updated based on the graph attention mechanism. Finally, combined with image-text aggregation layer to realize sentiment classification. The results of the experiments prove that the presented method is more effective than existing methods. In addition, a large-scale Twitter image-text sentiment analysis dataset was built by us and used in the experiments.

     

Comparison of immature and mature bone marrow-derived dendritic cells by atomic force microscopy

A comparative study of immature and mature bone marrow-derived dendritic cells (BMDCs) was first performed through an atomic force microscope (AFM) to clarify differences of their nanostructure and adhesion force. AFM images revealed that the immature BMDCs treated by granulocyte macrophage-colony stimulating factor plus IL-4 mainly appeared round with smooth surface, whereas the mature BMDCs induced by lipopolysaccharide displayed an irregular shape with numerous pseudopodia or lamellapodia and ruffles on the cell membrane besides becoming larger, flatter, and longer. AFM quantitative analysis further showed that the surface roughness of the mature BMDCs greatly increased and that the adhesion force of them was fourfold more than that of the immature BMDCs. The nano-features of the mature BMDCs were supported by a high level of IL-12 produced from the mature BMDCs and high expression of MHC-II on the surface of them. These findings provide a new insight into the nanostructure of the immature and mature BMDCs.