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Browsing by Author "Choudhury, Muntabir"

Now showing 1 - 2 of 2
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    Automatic Metadata Extraction Incorporating Visual Features from Scanned Electronic Theses and Dissertations
    Choudhury, Muntabir; Jayanetti, Himarsha R.; Wu, Jian; Ingram, William A.; Fox, Edward (IEEE, 2021-09-27)
    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 such as GROBID, CERMINE, and ParsCit, so they often fail to extract metadata from scanned documents such as for 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 baseline 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.
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    A Study of Computational Reproducibility using URLs Linking to Open Access Datasets and Software
    Salsabil, Lamia; Wu, Jian; Choudhury, Muntabir; Ingram, William A.; Fox, Edward A.; Rajtmajer, Sarah; Giles, C. Lee (ACM, 2022-04-25)
    Datasets and software packages are considered important resources that can be used for replicating computational experiments. With the advocacy of Open Science and the growing interest of investigating reproducibility of scientific claims, including URLs linking to publicly available datasets and software packages has become an institutionalized part of research publications. In this preliminary study, we investigated the disciplinary dependency and chronological trends of including open access datasets and software (OADS) in electronic theses and dissertations (ETDs), based on a hybrid classifier called OADSClassifier, consisting of a heuristic and a supervised learning model. The classifier achieves the best F1 of 0.92.We found that the inclusion of OADS-URLs exhibited a strong disciplinary dependence and the fraction of ETDs containing OADS-URLs has been gradually increasing over the past 20 years.We developed and share a ground truth corpus consisting of 500 manually labeled sentences containing URLs from scientific papers. The dataset and source code are available at https://github.com/lamps-lab/oadsclassifier.