The impact of diverse, alternative representations of textual input data on the quality and computational demands of selected classification models in datasets containing specialized vocabulary

This paper investigates the impact of various representations of textual input data on the quality of selected classification models in datasets containing specialized vocabulary. By employing various vectorization techniques and machine learning models, the study aims to improve our understanding of the relationship between textual representations and model performance and efficiency.