Abstract 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 …

In this paper, we present MADAMIRA, a system for morphological analysis and
disambiguation of Arabic that combines some of the best aspects of two previously …

This paper reports on the first shared task on statistical parsing of morphologically rich lan-
guages (MRLs). The task features data sets from nine languages, each available both in …

Segmentation of clitics has been shown to improve accuracy on a variety of Arabic NLP
tasks. However, state-of-the-art Arabic word segmenters are either limited to formal Modern …

This paper presents a model for Arabic morphological disambiguation based on Recurrent
Neural Networks (RNN). We train Long Short-Term Memory (LSTM) cells in several …

We present state-of-the-art results on morphosyntactic tagging across different varieties of
Arabic using fine-tuned pre-trained transformer language models. Our models consistently …

In order to accurately represent the meaning and pronunciation of Arabic words and
sentences, the presence of diacritics plays a crucial role. Over the years, researchers have …

We introduce CALIMA-Star, a very rich Arabic morphological analyzer and generator that
provides functional and form-based morphological features as well as built-in tokenization …

While online Arabic is primarily written using the Arabic script, a Roman-script variety called
Arabizi is often seen on social media. Although this representation captures the phonology …

With the advent of pre-trained language models, many natural language processing tasks in
various languages have achieved great success. Although some research has been …