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A novel text representation which enables image classifiers to also simultaneously classify text, applied to name disambiguation

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  • Stephen M. Petrie

    (Swinburne University of Technology)

  • T’Mir D. Julius

    (Swinburne University of Technology)

Abstract

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, obtaining a list of IDs which identify individual inventors with high accuracy. 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. The trained neural network then labels each pair of records as either matched (same inventor) or non-matched (different inventors), producing highly accurate results. Our new text-to-image representation method could also be used more broadly for other text comparison problems, such as entity disambiguation of academic publications, or for problems that require simultaneous classification of both text and image datasets.

Suggested Citation

  • Stephen M. Petrie & T’Mir D. Julius, 2024. "A novel text representation which enables image classifiers to also simultaneously classify text, applied to name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(2), pages 719-743, February.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:2:d:10.1007_s11192-023-04712-7
    DOI: 10.1007/s11192-023-04712-7
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    1. Bronwyn H. Hall & Adam B. Jaffe & Manuel Trajtenberg, 2001. "The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools," NBER Working Papers 8498, National Bureau of Economic Research, Inc.
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    3. Ventura, Samuel L. & Nugent, Rebecca & Fuchs, Erica R.H., 2015. "Seeing the non-stars: (Some) sources of bias in past disambiguation approaches and a new public tool leveraging labeled records," Research Policy, Elsevier, vol. 44(9), pages 1672-1701.
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