Language models (LMs) are pretrained on diverse data sources, including news, discussion
forums, books, and online encyclopedias. A significant portion of this data includes opinions …

Evaluating the factual consistency of automatically generated summaries is essential for the
progress and adoption of reliable summarization systems. Despite recent advances, existing …

The political stance prediction for news articles has been widely studied to mitigate the echo
chamber effect–people fall into their thoughts and reinforce their pre-existing beliefs. The …

Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional
spaces and have become the de-facto standard for knowledge graph completion. Most …

With the advent of pretrained language models (LMs), increasing research efforts have been
focusing on infusing commonsense and domain-specific knowledge to prepare LMs for …

Modeling the ideological perspectives of political actors is an essential task in computational
political science with applications in many downstream tasks. Existing approaches are …

Drug repurposing (DR) based on knowledge graphs (KGs) is challenging, which uses
knowledge graph reasoning models to predict new therapeutic pathways for existing drugs …

We address an important gap in detecting political bias in news articles. Previous works that
perform document classification can be influenced by the writing style of each news outlet …

Large, diachronic datasets of political discourse are hard to come across, especially for
resource-lean languages such as Greek. In this paper, we introduce a curated dataset of the …

Knowledge graph (KG) has been used to provide additional relational knowledge about
stance targets, thereby enhancing stance detection performance. However, the utilization …