Dawei Song

TL;DR: Wang et al. as discussed by the authors proposed a Graph Convolutional Network (GCN) over the dependency tree of a sentence to exploit syntactical information and word dependencies, and a novel aspect-specific sentiment classification framework was raised.

TL;DR: Term relationships are used to expand query model instead of document model, so that query expansion process can be naturally implemented and more sophisticated inferential relationships extracted with Information Flow are exploited.

TL;DR: A preprocessing method that encapsulates the multi-channel neurophysiological signals into grid-like frames through wavelet and scalogram transform and a hybrid deep learning model that combines the ‘Convolutional Neural Network’ and ‘Recurrent Neural Network (RNN)’, for extracting task-related features, mining inter-channel correlation and incorporating contextual information from those frames are proposed.

TL;DR: The results of this paper validate the possibility of exploring robust EEG features in cross-subject emotion recognition with a wider range of feature types, including 18 kinds of linear and non-linear EEG features.

TL;DR: This work proposes to build a Graph Convolutional Network (GCN) over the dependency tree of a sentence to exploit syntactical information and word dependencies and raises a novel aspect-specific sentiment classification framework.