… Later, a machine learning approach to the problem of … The corpus contains journals, mostly
from the Biomedical domain, … Word sense disambiguation based on constrained random …

… Finally, we examine and discuss some of the current issues, … machine learning-based word
sense disambiguation method … embeddings for the biomedical domain. These embeddings …

… MoE is a deep learning technique where multiple expert … model by training it on “in-domain”
biomedical datasets where … These reasoning and word sense disambiguation capabilities …

… areas of application of Deep Learning related techniques, we … the task a distinct nature and
domain of application. In light of … to the issue entailed by assuming word senses have discrete …

… it the largest WiC dataset in the biomedical domain. We … word sense disambiguation (WSD)
12 are two NLP tasks trying to address the issue of semantic representation in the biomedical …

… (WSD) is a wellresearched problem in Natural Language Pro… artificial datasets for word
sense disambiguation tasks by … For example, a machine learning scientist would likely be able …

… Belonging to the first category, we find general problems … facilitate research in the
biomedicine domain. The benchmark … The shared tasks on Word Sense Disambiguation (WSD) …

… have an overall review of word sense disambiguation approaches, strategies and issues, in
… The supervised approach operates based on the machine learning technique and consists …

… problem, along with conclusions on proper training setup and processes for automatic
collection of an annotated dataset for biomedical word sense disambiguation. … to deep learning. …

… on a variety of deep learning topics within health informatics, … Then we had two domain
experts shorten the publication list … used deep learning architectures in NLP, word embeddings, …