  The widespread absence of diacritical marks in Arabic text poses a
significant challenge for Arabic natural language processing (NLP). This paper
explores instances of naturally occurring diacritics, referred to as
"diacritics in the wild," to unveil patterns and latent information across six
diverse genres: news articles, novels, children's books, poetry, political
documents, and ChatGPT outputs. We present a new annotated dataset that maps
real-world partially diacritized words to their maximal full diacritization in
context. Additionally, we propose extensions to the analyze-and-disambiguate
approach in Arabic NLP to leverage these diacritics, resulting in notable
improvements. Our contributions encompass a thorough analysis, valuable
datasets, and an extended diacritization algorithm. We release our code and
datasets as open source.
