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 (F1: 99.09%, precision: 99.41%, recall: 98.76%). Our new text-to-image representation method could potentially 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 images.
We introduce a novel text representation method which enables image classifiers to be applied to text classification problems, and apply the method to inventor name disambiguation. A method to map a pair of textual information into a 2D RGB image that can be fed to 2D convoutional neural networks (image classifiers). The authors consider the problem of names disambiguisation for patent names inventors and propose to build an image page representation of the two name strings to compare and to apply an image classifier.