1 citations · 1 across the 4 of their papers we have counts for
5 papers
GSAP-ERE: Fine-Grained Scholarly Entity and Relation Extraction Focused on Machine Learning
Wolfgang Otto, Lu Gan, Sharmila Upadhyaya +2
Research in Machine Learning (ML) and AI evolves rapidly. Information Extraction (IE) from scientific publications enables to identify information about research concepts and resou…
NFDI4DS Shared Tasks for Scholarly Document Processing
Raia Abu Ahmad, Rana Abdulla, Tilahun Abedissa Taffa +18
Shared tasks are powerful tools for advancing research through community-based standardised evaluation. As such, they play a key role in promoting findable, accessible, interoperab…
Research Knowledge Graphs in NFDI4DataScience: Key Activities, Achievements, and Future Directions
Kanishka Silva, Marcel R. Ackermann, Heike Fliegl +10
As research in Artificial Intelligence and Data Science continues to grow in volume and complexity, it becomes increasingly difficult to ensure transparency, reproducibility, and d…
Utilizing Large Language Models for Named Entity Recognition in Traditional Chinese Medicine against COVID-19 Literature: Comparative Study
Xu Tong, Nina Smirnova, Sharmila Upadhyaya +5
Objective: To explore and compare the performance of ChatGPT and other state-of-the-art LLMs on domain-specific NER tasks covering different entity types and domains in TCM against…
Enhancing Software-Related Information Extraction via Single-Choice Question Answering with Large Language Models
Wolfgang Otto, Sharmila Upadhyaya, Stefan Dietze
This paper describes our participation in the Shared Task on Software Mentions Disambiguation (SOMD), with a focus on improving relation extraction in scholarly texts through gener…