3 citations · 7 across the 5 of their papers we have counts for
5 papers
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…
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…
EXmatcher: Combining Features Based on Reference Strings and Segments to Enhance Citation Matching
Behnam Ghavimi, Wolfgang Otto, Philipp Mayr
Citation matching is a challenging task due to different problems such as the variety of citation styles, mistakes in reference strings and the quality of identified reference segm…
Highly cited references in PLOS ONE and their in-text usage over time
Wolfgang Otto, Behnam Ghavimi, Philipp Mayr +2
In this article, we describe highly cited publications in a PLOS ONE full-text corpus. For these publications, we analyse the citation contexts concerning their position in the tex…