… in 1986 to solve the disambiguation problem by superimposing word sense definitions on the
… : An illustration of the role of bias in machine learning. arXiv preprint cmp-lg/9612001. [13] …

… deep learning, characterized by its “end-to-end” approach, can offer a more effective solution
to the problem … applications of neural networks across various domains [9]. Semantic …

… Word Sense Disambiguation (WSD) is an open problem in Natural … systems apply
machine learning techniques on a sense-… methods to analyse texts in the biomedical domain. …

… One of the main issues that those systems face is polysemy. … to the .fr domain, while using
medium frequency words from the Le … Note that CLEAR is a part of the NACHOS and biomed-fr …

… To address this issue, an Ontological Framework for IE (OFIE) … Summary machine learning
approaches, IE from research … because: • information extraction is highly domain-specific that …

… One of the major challenges in using deep learning models … Networks (GATs) for word sense
disambiguation, demonstrating … of our model on the accuracy of rare sense disambiguation. …

… the problem of supervised classification in word disambiguation … Mahmoodv and Hourali
[29] used the machine learning … supervised biomedical word sense disambiguation,” Journal of …

… learning the basics, benefits, and challenges of various word … systems, biomedical text mining,
and topic modeling. Pre-trained … to effectively describe a word's meaning and provide the …

… approach for word sense disambiguation is discussed. The most critical and identified
problem of natural … There are three suggested machine learning approaches for word sense …

… -shot learning problem to deal with insufficient training samples for tail … senses in Word
Sense Disambiguation (WSD). We design a collocation feature matching method for tail senses, …