  Deep learning models such as convolutional neural networks and recurrent
networks are widely applied in text classification. In spite of their great
success, most deep learning models neglect the importance of modeling context
information, which is crucial to understanding texts. In this work, we propose
the Adaptive Region Embedding to learn context representation to improve text
classification. Specifically, a metanetwork is learned to generate a context
matrix for each region, and each word interacts with its corresponding context
matrix to produce the regional representation for further classification.
Compared to previous models that are designed to capture context information,
our model contains less parameters and is more flexible. We extensively
evaluate our method on 8 benchmark datasets for text classification. The
experimental results prove that our method achieves state-of-the-art
performances and effectively avoids word ambiguity.
