Learning Local and Global Multicontext Representations for Document Classification Contextbased attention models achieve stateoftheart results on constructing document representations. Current methods build document representations based on a trainable global context vector for all documents, while local contexts capturing documentspecific information are ignored. Representing document context as a vector also restricts the modelu0027s capacity to capture complete semantic components of documents. In this work, we address these limitations by proposing novel attentionbased approaches. We first propose a local and global context attention (LGCA) model, which constructs the context representations of a document as a combination of global and local contexts. The local context is used to capture local documentspecific information. Furthermore, a multicontext attention (MCA) model is proposed to capture multiple local contexts within a document, thereby fusing the overall semantic information of documents. We conduct experiments on documentlevel sentiment classification tasks where extra user and product information is shown to be important. Experimental results indicate that our methods significantly outperform previous models with and without user and product information. It is also demonstrated that our model has great potential of handling extra information to improve document classification performance.

CrossMedia ImageText Retrieval Combined with Global Similarity and Local Similarity In this paper, we study the problem of imagetext matching in order to make the image and text have better semantic matching. In the previous work, people just simply used the pretraining network to extract image and text features and project directly into a common subspace, or change various loss functions on this basis, or use the attention mechanism to directly match the image region proposals and the text phrases. This is not a good match for the semantics of the image and the text. In this study, we propose a method of crossmedia retrieval based on global representation and local representation. We constructed a crossmedia twolevel network to explore better semantic matching between images and text, which contains subnets that handle both global and local features. Specifically, we not only use the selfattention network to obtain a macro representation of the global image but also use the local finegrained patch with the attention mechanism. Then, we use a twolevel alignment framework to promote each other to learn different representations of crossmedia retrieval. The innovation of this study lies in the use of more comprehensive features of image and text to design the two kinds of similarity and add them up in some way. Experimental results show that this method is effective in imagetext retrieval. Experimental results on the Flickr30K and MSCOCO datasets show that this model has a better recall rate than many of the current advanced crossmedia retrieval models.