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 realworld partially diacritized words to their maximal full diacritization in context.Additionally, we propose extensions to the analyze-anddisambiguate 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.

We present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python. CAMeL Tools currently provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and Sentiment Analysis. In this paper, we describe the design of CAMeL Tools and the functionalities it provides.