Web Science and Digital Libraries Research Group

Figure 1: EnSU Architecture. This is Figure 2 in our paper. This blog post summarizes our paper, " Context-Based URL Classification for Open Access Datasets and Software in Scholarly Documents ," ( preprint ) published in the 2025 ACM/IEEE Joint Conference on Digital Libraries (JCDL ’25) .  Many scholarly papers include URLs that point to open-access datasets and software ( OADS ), but the URL string alone rarely tells us what the link refers to. In this paper, we present EnSU , an ensemble of three complementary models for classifying these URLs using the surrounding citation context. EnSU assigns each URL to one of six categories that jointly reflect both the resource type (dataset vs. software) and resource provider (authors vs. third parties), plus two catch-all categories for projects and general links. On our  OADS-1K dataset, EnSU achieves a macro-average F1-score of up to 0.90 on a stratified 80/20 split and a mean macro-average F1-score of 0.89 across five-fold c...

Contrastive learning models proposed in recent years (Figure 1 from A Simple Framework for Contrastive Learning of Visual Representations)  Supervised learning has been widely used in machine learning, which requires the input data (for example, images) be annotated (for example, classes). We can use supervised learning to build models to find the relationship between input data and annotated labels and then use the models to predict labels for future inputs. This is the way in traditional machine learning, such as decision tree, logistic regression, SVM, and neural networks. It's costly to create annotations, and as a result for most of the circumstances  annotation is not available . We need a method which can make use of  tons of data in real life without annotation , which is called unsupervised learning . Conventional u nsupervised learning includes clustering (for example, k-means ), dimension reduction (for example, p rincipal component analysis ) and so...

  Figure 1: An example of claim sentences (Figure 1 in  ClaimDistiller: Scientific Claim Extraction with Supervised Contrastive Learning ) In the summer of 2023, we published a paper titled " ClaimDistiller: Scientific Claim Extraction with Supervised Contrastive Learning " at the Joint Workshop of the 4th Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2023) and the 3rd AI + Informatics (AII 2023) co-located with JCDL 2023 . In this paper, we explored ways to automatically locate key claims from abstracts in academic papers. One challenge of this task is the limited publicly available training data. We compared transfer learning and contrastive learning and found that contrastive learning outperformed transfer learning at a lower cost of training data. We proposed a contrastive-learning-based model, ClaimDistiller, with the highest performance among all methods which has an F1 score as high as 87.45%...