A Morphologically Annotated Corpus of Emirati Arabic

A Morphologically Annotated Corpus of Emirati Arabic Salam Khalifa, Nizar Habash, Fadhl Eryani, Ossama Obeid, Dana Abdulrahim,† Meera Al Kaabi‡ Computational Approaches to Modeling Language Lab, New York University Abu Dhabi, UAE University of Bahrain, Bahrain† The United Arab Emirates University, UAE‡ {salamkhalifa@nyu.edu, nizar.habash@nyu.edu, darahim@uob.edu.bh} Abstract We present an ongoing effort on the first large-scale morphologically manually annotated corpus of Emirati Arabic. This corpus includes about 200,000 words selected from eight Gumar corpus novels in the Emirati Arabic variety. The selected texts are being annotated for tokenization, part-of-speech, lemmatization, English glosses and dialect identification. The orthography of the text is also adjusted for errors and inconsistencies. We discuss the guidelines for each part of the annotation components, and the annotation interface we use. We report on the quality of the annotation through an inter-annotator agreement measure. Keywords: Gulf Arabic, Part-of-Speech Tagging, Morphology, Annotation 1. Introduction There has been an increasing number of natural language processing (NLP) efforts focusing on dialectal Arabic, es- pecially with the increasing amounts of written material on the web. However, resources for dialectal Arabic NLP tasks such as part-of-speech (POS) tagging, morphological anal- ysis and disambiguation are still lacking compared to those for Modern Standard Arabic (MSA). MSA is the official language in more than 20 countries, where it is used in offi- cial communications, news, and education. Yet, it is not the commonly spoken variety of Arabic; the dialectal varieties of Arabic are what is used in the day-to-day communica- tion. Dialectal Arabic is also commonly used in written form on social media platforms, forums and blogs. Using available resources developed for MSA such as POS taggers and tokenizers gives limited performance when used on dialectal Arabic (Habash and Rambow, 2006; Jarrar et al., 2014; Khalifa et al., 2016a). Many researchers moved into the direction of creating tools and resources targeting the dialects specifically. Egyptian Arabic is one of the dialects that received earlier efforts for developing tools and resources. More resources are being developed for other dialects such as Levantine, Tunisian, Moroccan and Yemeni Arabic. Gulf Arabic, as we define it to be the native spoken variety in the Gulf Cooperation Council, is still lagging behind other Arabic dialects with respect to re- source and tool creation, given the considerable amount of dialectal content online. In this paper, we present an ongoing project for creating a manually annotated corpus of about 200,000 words of the Gulf Arabic of the United Arab Emirates – Emirati Arabic. The corpus is annotated for tokenization, POS, lemmas and English glosses in addition to spelling conventionalization and dialect identification. This resource will support the de- velopment of Arabic dialect enabling technologies, such as automatic POS tagging and morphological disambiguation, which in turn will facilitate efforts on different NLP tasks such as machine translation. The rest of this paper is organized as follows. We discuss related work on dialectal corpora in Section 2. In Section 3. we describe the corpus used in this effort. We then present the annotation guidelines …

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