activity
20212026
most citedDomain Adaptation of Multilingual Semantic Search -- Literature Review

1 citations · 3 across the 9 of their papers we have counts for

collaborators

13 papers

cs.CL2026

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…

cs.CL2026

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…

cs.CL2025

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…

cs.CL2025★ 1 cited

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…

cs.CL2025

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…

cs.CL2025

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)…

cs.CL2024

Automated Collection of Evaluation Dataset for Semantic Search in Low-Resource Domain Language

Anastasia Zhukova, Christian E. Matt, Bela Gipp

Domain-specific languages that use a lot of specific terminology often fall into the category of low-resource languages. Collecting test datasets in a narrow domain is time-consumi…

cs.IR2024★ 1 cited

Domain Adaptation of Multilingual Semantic Search -- Literature Review

Anna Bringmann, Anastasia Zhukova

This literature review gives an overview of current approaches to perform domain adaptation in a low-resource and approaches to perform multilingual semantic search in a low-resour…

cs.CL2023

Generative User-Experience Research for Developing Domain-specific Natural Language Processing Applications

Anastasia Zhukova, Lukas von Sperl, Christian E. Matt +1

User experience (UX) is a part of human-computer interaction (HCI) research and focuses on increasing intuitiveness, transparency, simplicity, and trust for the system users. Most…

cs.CL2021★ 1 cited

ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts

Anastasia Zhukova, Felix Hamborg, Bela Gipp

Named entity recognition (NER) is an important task that aims to resolve universal categories of named entities, e.g., persons, locations, organizations, and times. Despite its com…

cs.CY2021

Newsalyze: Effective Communication of Person-Targeting Biases in News Articles

Felix Hamborg, Kim Heinser, Anastasia Zhukova +2

Media bias and its extreme form, fake news, can decisively affect public opinion. Especially when reporting on policy issues, slanted news coverage may strongly influence societal…

cs.CL2021

XCoref: Cross-document Coreference Resolution in the Wild

Anastasia Zhukova, Felix Hamborg, Karsten Donnay +1

Datasets and methods for cross-document coreference resolution (CDCR) focus on events or entities with strict coreference relations. They lack, however, annotating and resolving co…

cs.CL2021

Towards Evaluation of Cross-document Coreference Resolution Models Using Datasets with Diverse Annotation Schemes

Anastasia Zhukova, Felix Hamborg, Bela Gipp

Established cross-document coreference resolution (CDCR) datasets contain event-centric coreference chains of events and entities with identity relations. These datasets establish…