Document Type

Conference Paper

Publication Date

2021

Publication Title

Proceedings of 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL '21)

Pages

1-4

Conference Name

2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL '21), September 27-30, 2021, Virtual, Online

Abstract

Electronic Theses and Dissertations (ETDs) contain domain knowledge that can be used for many digital library tasks, such as analyzing citation networks and predicting research trends. Automatic metadata extraction is important to build scalable digital library search engines. Most existing methods are designed for born-digital documents, so they often fail to extract metadata from scanned documents such as ETDs. Traditional sequence tagging methods mainly rely on text-based features. In this paper, we propose a conditional random field (CRF) model that combines text-based and visual features. To verify the robustness of our model, we extended an existing corpus and created a new ground truth corpus consisting of 500 ETD cover pages with human validated metadata. Our experiments show that CRF with visual features outperformed both a heuristic and a CRF model with only text-based features. The proposed model achieved 81.3%-96% F1 measure on seven metadata fields. The data and source code are publicly available on Google Drive1 and a GitHub repository2, respectively.

Comments

The DOI on this article is non-functional.

Rights

© 2021 Held by the owner/authors.

Included with the kind written permission of the author.

Data Availability

Article states: The data and source code are publicly available on Google Drive and GitHub repository respectively.

Original Publication Citation

Choudhury, M. H., Jayanetti, H. R., Wu, J., Ingram, W. A., & Fox, E. A. (2021). Automatic metadata extraction incorporating visual features from scanned electronic theses and dissertations [Paper presentation]. Proceedings of 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL '21). Virtual, Online.

ORCID

0000-0002-9318-8844 (Choudhury), 0000-0003-4748-9176 (Jayanetti), 0000-0003-0173-4463 (Wu)

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