Arabic diacritics are signs used in Arabic orthography to represent essential
morphophonological and syntactic information. It is a common practice to leave out those …

In this work, we present several deep learning models for the automatic diacritization of
Arabic text. Our models are built using two main approaches, viz. Feed-Forward Neural …

Accurate assignment of meaning to a word based on its context, known as Word Sense
Disambiguation (WSD), remains challenging across languages. Extensive research aims to …

Word Sense Disambiguation (WSD) aims to predict the correct sense of a word given its
context. This problem is of extreme importance in Arabic, as written words can be highly …

Diacritic restoration has gained importance with the growing need for machines to
understand written texts. The task is typically modeled as a sequence labeling problem and …

We propose a novel multitask learning method for diacritization which trains a model to both
diacritize and translate. Our method addresses data sparsity by exploiting large, readily …

Arabic diacritics play a significant role in distinguishing words with the same orthography but
different meanings, pronunciations, and syntactic functions. The presence of Arabic diacritics …

Diacritization plays a pivotal role in improving readability and disambiguating the meaning
of Arabic texts. Efforts have so far focused on marking every eligible character (Full …

Text classification is a fundamental task in natural language processing, and has been
widely studied for various languages. However, Arabic text classification is challenging due …

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 …