  We introduce a novel method for converting text data into abstract image
representations, which allows image-based processing techniques (e.g. image
classification networks) to be applied to text-based comparison problems. We
apply the technique to entity disambiguation of inventor names in US patents.
The method involves converting text from each pairwise comparison between two
inventor name records into a 2D RGB (stacked) image representation. We then
train an image classification neural network to discriminate between such
pairwise comparison images, and use the trained network to label each pair of
records as either matched (same inventor) or non-matched (different inventors),
obtaining highly accurate results. Our new text-to-image representation method
could also be used more broadly for other NLP comparison problems, such as
disambiguation of academic publications, or for problems that require
simultaneous classification of both text and image datasets.
