… Word sense disambiguation is a core research field in … interest of artificial intelligence and
machine learning scientists and … to address the problem of word sense disambiguation. The …

… Recent studies in the biomedical domain incorporate expert-… for disambiguation of biomedical
text data, a deep learning–… of concepts prevents the issue of sparsity (of word features) in …

… WSD is a challenging artificial intelligence problem that has … PubMed articles which are
biomedical domain-specific. MEDLINE … We tested five machine learning classifiers from the scikit-…

… for addressing the Word Sense Disambiguation (WSD) problem in the biomedical domain.
This … point of view, through the use of classical machine learning algorithms [8], and in the last …

… , deep learning methods have been applied to many natural language processing tasks to
achieve state-of-the-art performance. However, in the biomedical domain, … some issues of the …

… To overcome this problem, and therefore to enhance the … , we are introducing the Word
Sense Disambiguation task and … in machine learning, to classify a document, we build machine …

… ), to the problem of word sense disambiguation in clinical text. … In this study, we used three
biomedical corpora to evaluate … describe a novel interactive machine learning framework that …

… and word vectors learned through unsupervised deep learning … [28] used an active learning
strategy to involve domain … These dense word vectors obviate the sparsity issues inherent …

… (WSD), it outlines diverse approaches ranging from deep learning … The major problem this
paper intends to tackle is lexical … of neural networks for biomedical text disambiguation, from …

… To address this issue, the machine learning community has … On one biomedical literature
corpus and two clinical notes … machine learning algorithm that can learn from domain …