A novel text representation which enables image classifiers to also simultaneously classify text, applied to name disambiguation
Bibliographic Data
| ID | 21442091 |
|---|---|
| Authors | Stephen M Petrie (0000-0002-8773-516X, Swinburne University of Technology, corresponding author), T’Mir D Julius, T’Mir Julius (Swinburne University of Technology) |
| Year | 2024 |
| Volume | 129 |
| Issue | 2 |
| Pages | 719-743 |
| Publication date | 2024-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Scientometrics (JOURNAL) |
| Journal identifiers | ISSN: 0138-9130 • E-ISSN: 1588-2861 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11192-023-04712-7 |
| OpenAlex | W4379473490 |
| Language | EN |
| References cited | 23 |
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
Artificial neural network · Contextual image classification · Image (mathematics) · Information retrieval · Natural language processing · Pairwise comparison · Pattern recognition (psychology) · Representation (politics) · Artificial Intelligence · Biomedical Text Mining and Ontologies · Computer Science · Handwritten Text Recognition Techniques · Image Retrieval and Classification Techniques
| Citation velocity | historical |
|---|---|
| Highly cited | No |