… 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 …

… 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 …

… , 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 …

… A wide range of Word Sense Disambiguation (WSD) approaches … problems, we suggest a
semisupervised neural biomedical … Sense disambiguation methods in the biomedical domain …

… (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 …

… machine learning algorithms to biomedical WSD, including Naive Bayes and decision lists,
adaptation of decision lists, and mixed supervised learning … problem in a biomedical domain. …

… Faced with these challenges, we need to design a novel and … WSD models based on deep
learning technology [6]. One is … over WSD in biomedical domain is evaluated. Cao proposes …

… The most important contributions of this paper are: 1) We build graphs from terms instead of
concepts, which bypasses the problem of manual disambiguation of the mapping tools when …

… in existence during this whole domain; these studies were … most difficult problems is word
sense disambiguation (WSD). … Disambiguating ambiguous biomedical terms in biomedical …

… ) by testing traditional supervised machine learning algorithms … to sample a specific problem
in the NLP domain and most of … biomedical corpora which are used in the clinical domain: …