We developed precompiled lexicons and classification rules as features for the following ML classifiers: logistic regression, random forest, and support vector machines (SVMs). These features were ...

Recent advances in automatic question generation (AQG) coupled with the evolution of natural language processing (NLP) have significantly transformed educational assessments and interactive learning ...

Start working toward program admission and requirements right away. Work you complete in the non-credit experience will transfer to the for-credit experience when you ...

Start working toward program admission and requirements right away. Work you complete in the non-credit experience will transfer to the for-credit experience when you ...

Treatment of non–muscle-invasive bladder cancer (NMIBC) is guided by risk stratification using clinical and pathologic criteria. This study aimed to develop a natural language processing (NLP) model ...

In the first half of this course, we will explore the evolution of deep neural network language models, starting with n-gram models and proceeding through feed-forward neural networks, recurrent ...

Recently, a research team at The University of Massachusetts Amherst led by Emma Strubell published a paper on the carbon emissions generated through training a high-performing NLP model. Given the ...

The course explores a breadth of Natural Language Processing (NLP) applications with a focus on contemporary, state-of-the-art systems, often based on deep learning techniques. Topics include word ...

Large language models represent text using tokens, each of which is a few characters. Short words are represented by a single token (like “the” or “it”), whereas larger words may be represented by ...

Development of an Electronic Health Record–Based Algorithm for Predicting Lung Cancer Screening Eligibility in the Population-Based Research to Optimize the Screening Process Lung Research Consortium ...