Several studies have shown that the use of embeddings improves outcomes in many NLP activities, including text categorization. In this paper, we focus on how word embeddings can be used on newspaper articles about crimes to categorize them according to the type of crime they report. Our approach was tested on an Italian dataset of 15,361 crime news articles combining different Word2Vec models and exploiting supervised and unsupervised Machine Learning categorization algorithms. The tests show very promising results.
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