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 commonly used systems for Arabic processing, MADA (Habash and Rambow, 2005; Habash et al., 2009; Habash et al., 2013) and AMIRA (Diab et al., 2007). MADAMIRA improves upon the two systems with a more streamlined Java implementation that is more robust, portable, extensible, and is faster than its ancestors by more than an order of magnitude. We also discuss an online demo (see http://nlp.ldeo.columbia.edu/madamira/) that highlights these aspects.

In this paper, we define the task of Number Identification in natural context. We present and validate a language-independent semi-automatic approach to quickly building a gold standard for evaluating number identification systems by exploiting hand-aligned parallel data. We also present and extensively evaluate a robust rule-based system for number identification in natural context for Arabic for a variety of number formats and types. The system is shown to have strong performance, achieving, on a blind test, a 94.8% F-score for the task of correctly identifying number expression spans in natural text, and a 92.1% F-score for the task of correctly determining the core numerical value.