1 citations · 3 across the 9 of their papers we have counts for
13 papers
Diverse Word Choices, Same Reference: Annotating Lexically-Rich Cross-Document Coreference
Anastasia Zhukova, Felix Hamborg, Karsten Donnay +2
Cross-document coreference resolution (CDCR) identifies and links mentions of the same entities and events across related documents, enabling content analysis that aggregates infor…
Piecing Together Cross-Document Coreference Resolution Datasets: Systematic Dataset Analysis and Unification
Anastasia Zhukova, Terry Ruas, Jan Philip Wahle +1
Research in CDCR remains fragmented due to heterogeneous dataset formats, varying annotation standards, and the predominance of the CDCR definition as the event coreference resolut…
Contrastive Learning Using Graph Embeddings for Domain Adaptation of Language Models in the Process Industry
Anastasia Zhukova, Jonas Lührs, Christian E. Lobmüller +1
Recent trends in NLP utilize knowledge graphs (KGs) to enhance pretrained language models by incorporating additional knowledge from the graph structures to learn domain-specific t…
What's in the News? Towards Identification of Bias by Commission, Omission, and Source Selection (COSS)
Anastasia Zhukova, Terry Ruas, Felix Hamborg +2
In a world overwhelmed with news, determining which information comes from reliable sources or how neutral is the reported information in the news articles poses a challenge to new…
Link Prediction for Event Logs in the Process Industry
Anastasia Zhukova, Thomas Walton, Christian E. Lobmüller +1
In the era of graph-based retrieval-augmented generation (RAG), link prediction is a significant preprocessing step for improving the quality of fragmented or incomplete domain-spe…
Efficient Domain-adaptive Continual Pretraining for the Process Industry in the German Language
Anastasia Zhukova, Christian E. Matt, Bela Gipp
Domain-adaptive continual pretraining (DAPT) is a state-of-the-art technique that further trains a language model (LM) on its pretraining task, e.g., masked language modeling (MLM)…