短文本分类是一种使用预定义标签对短句子进行分类的方法。 但是，短文本的长度短受到限制，这导致特征稀疏的挑战性问题。 现有的大多数方法都将每个短句子视为独立且均匀分布的（IID），仅在句子本身中集中了局部上下文，并且丢失了句子之间的关系信息。 为了克服这些限制，我们提出了一个PathWalk模型，该模型结合了图网络和短句子的强度来解决短文本的稀疏性。 在四个不同的可用数据集上的实验结果表明，我们的PathWalk方法达到了最新的结果，证明了图形网络在短文本分类中的效率和鲁棒性。
Recognition accuracy and response time are both critically essential ahead of building practical electroencephalography (EEG) based brain-computer interface (BCI). Recent approaches, however, have either compromised in the classification accuracy or responding time. This paper presents a novel deep learning approach designed towards remarkably accurate and responsive motor imagery (MI) recognition based on scalp EEG. Bidirectional Long Short-term Memory (BiLSTM) with the Attention mechanism manages to derive relevant features from raw EEG signals. The connected graph convolutional neural network (GCN) promotes the decoding performance by cooperating with the topological structure of features, which are estimated from the overall data. The 0.4-second detection framework has shown effective and efficient prediction based on individual and group-wise training, with 98.81% and 94.64% accuracy, respectively, which outperformed all the state-of-the-art studies. The introduced deep feature mining approach can precisely recognize human motion intents from raw EEG signals, which paves the road to translate the EEG based MI recognition to practical BCI systems.