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We study text representation methods using deep models. Current methods, suchas word-level embedding and character-level embedding schemes, treat texts aseither a sequence of atomic words or a sequence of characters. These methodseither ignore word morphologies or word boundaries. To overcome theselimitations, we propose to convert texts into 2-D representations and developthe Sent2Matrix method. Our method allows for the explicit incorporation ofboth word morphologies and boundaries. When coupled with a novel serpentinepadding method, our Sent2Matrix method leads to an interesting visualization inwhich 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 methodconsistently outperforms prior embedding methods.

Sent2Matrix: Folding Character Sequences in Serpentine Manifolds for Two-Dimensional Sentence