  We study text representation methods using deep models. Current methods, such
as word-level embedding and character-level embedding schemes, treat texts as
either a sequence of atomic words or a sequence of characters. These methods
either ignore word morphologies or word boundaries. To overcome these
limitations, we propose to convert texts into 2-D representations and develop
the Sent2Matrix method. Our method allows for the explicit incorporation of
both word morphologies and boundaries. When coupled with a novel serpentine
padding method, our Sent2Matrix method leads to an interesting visualization in
which 1-D character sequences are folded into 2-D serpentine manifolds.
Notably, our method is the first attempt to represent texts in 2-D formats.
Experimental results on text classification tasks shown that our method
consistently outperforms prior embedding methods.
