Recently, there has been enormous progress in coreference resolution. These recent developments were applied to Chinese, English and other languages, with outstanding results. However, languages with a rich morphology or fewer resources, such as Arabic, have not received as much attention. In fact, when this PhD work started there was no neural coreference resolver for Arabic, and we were not aware of any learning-based coreference resolver for Arabic since [Björkelund and Kuhn, 2014]. In addition, as far as we know, whereas lots of attention had been devoted to the phemomenon of zero anaphora in languages such as Chinese or Japanese, no neural model for Arabic zero-pronoun anaphora had been developed. In this thesis, we report on a series of experiments on Arabic coreference resolution in general and on zero anaphora in particular. We propose a new neural coreference resolver for Arabic, and we present a series of models for identifying and resolving Arabic zero pronouns. Our approach for zero-pronoun identification and resolution is applicable to other languages, and was also evaluated on Chinese, with results surpassing the state of the art at the time. This research also involved producing revised versions of standard datasets for Arabic coreference
