1 citations · 1 across the 4 of their papers we have counts for
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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…
Hidden Entity Detection from GitHub Leveraging Large Language Models
Lu Gan, Martin Blum, Danilo Dessi +3
Named entity recognition is an important task when constructing knowledge bases from unstructured data sources. Whereas entity detection methods mostly rely on extensive training d…
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…
GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity Extraction Focused on Machine Learning Models and Datasets
Wolfgang Otto, Matthäus Zloch, Lu Gan +2
Named Entity Recognition (NER) models play a crucial role in various NLP tasks, including information extraction (IE) and text understanding. In academic writing, references to mac…