Improving Text Relationship Modeling with Artificial Data

• URL: http://arxiv.org/abs/2010.14640v1
• Date: Tue, 27 Oct 2020 22:04:54 GMT
• Title: Improving Text Relationship Modeling with Artificial Data
• Authors: Peter Organisciak, Maggie Ryan
• Abstract summary: We apply and evaluate a synthetic data approach to relationship classification in digital libraries. We find that for classification on whole-part relationships between books, synthetic data improves a deep neural network classifier by 91%.
• Score: 0.07614628596146598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data augmentation uses artificially-created examples to support supervised machine learning, adding robustness to the resulting models and helping to account for limited availability of labelled data. We apply and evaluate a synthetic data approach to relationship classification in digital libraries, generating artificial books with relationships that are common in digital libraries but not easier inferred from existing metadata. We find that for classification on whole-part relationships between books, synthetic data improves a deep neural network classifier by 91%. Further, we consider the ability of synthetic data to learn a useful new text relationship class from fully artificial training data.

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