1 citations · 1 across the 6 of their papers we have counts for
6 papers
Evaluating Embedding Frameworks for Scientific Domain
Nouman Ahmed, Ronin Wu, Victor Botev
Finding an optimal word representation algorithm is particularly important in terms of domain specific data, as the same word can have different meanings and hence, different repre…
Fast, Accurate and Interpretable Graph Classification with Topological Kernels
Adam Wesołowski, Ronin Wu, Karim Essafi
We introduce a novel class of explicit feature maps based on topological indices that represent each graph by a compact feature vector, enabling fast and interpretable graph classi…
Benchmarking and Analyzing In-context Learning, Fine-tuning and Supervised Learning for Biomedical Knowledge Curation: a focused study on chemical entities of biological interest
Emily Groves, Minhong Wang, Yusuf Abdulle +4
Automated knowledge curation for biomedical ontologies is key to ensure that they remain comprehensive, high-quality and up-to-date. In the era of foundational language models, thi…
Searching for Carriers of the Diffuse Interstellar Bands Across Disciplines, using Natural Language Processing
Corentin van den Broek d'Obrenan, Frédéric Galliano, Jeremy Minton +2
The explosion of scientific publications overloads researchers with information. This is even more dramatic for interdisciplinary studies, where several fields need to be explored.…
Leveraging knowledge graphs to update scientific word embeddings using latent semantic imputation
Jason Hoelscher-Obermaier, Edward Stevinson, Valentin Stauber +4
The most interesting words in scientific texts will often be novel or rare. This presents a challenge for scientific word embedding models to determine quality embedding vectors fo…
Domain-adaptation of spherical embeddings
Mihalis Gongolidis, Jeremy Minton, Ronin Wu +3
Domain adaptation of embedding models, updating a generic embedding to the language of a specific domain, is a proven technique for domains that have insufficient data to train an…