  The dominant text classification studies focus on training classifiers using
textual instances only or introducing external knowledge (e.g., hand-craft
features and domain expert knowledge). In contrast, some corpus-level
statistical features, like word frequency and distribution, are not well
exploited. Our work shows that such simple statistical information can enhance
classification performance both efficiently and significantly compared with
several baseline models. In this paper, we propose a classifier with gate
mechanism named Adaptive Gate Attention model with Global Information (AGA+GI),
in which the adaptive gate mechanism incorporates global statistical features
into latent semantic features and the attention layer captures dependency
relationship within the sentence. To alleviate the overfitting issue, we
propose a novel Leaky Dropout mechanism to improve generalization ability and
performance stability. Our experiments show that the proposed method can
achieve better accuracy than CNN-based and RNN-based approaches without global
information on several benchmarks.
