Sent2Matrix: Folding Character Sequences in Serpentine Manifolds for Two-Dimensional Sentence
This addresses text representation for natural language processing by introducing a novel 2-D approach, though it appears incremental as it builds on existing embedding methods.
The paper tackled the problem of text representation by converting texts into 2-D formats to incorporate both word morphologies and boundaries, resulting in consistent outperformance over prior embedding methods in text classification tasks.
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.