MADARi: A Web Interface for Joint Arabic Morphological Annotation and Spelling Correction

MADARi: A Web Interface for Joint Arabic Morphological Annotation and Spelling Correction Ossama Obeid, Salam Khalifa, Nizar Habash, Houda Bouamor,† Wajdi Zaghouani,⇤ Kemal Oflazer† Computational Approaches to Modeling Language Lab, New York University Abu Dhabi, UAE † Carnegie Mellon University in Qatar, Qatar ⇤ Hamad Bin Khalifa University, Qatar {oobeid, salamkhalifa, nizar.habash}@nyu.edu, hbouamor@qatar.cmu.edu, wzaghouani@hbku.edu.qa, ko@cs.cmu.edu Abstract In this paper, we introduce MADARi, a joint morphological annotation and spelling correction system for texts in Standard and Dialectal Arabic. The MADARi framework provides intuitive interfaces for annotating text and managing the annotation process of a large number of sizable documents. Morphological annotation includes indicating, for a word, in context, its baseword, clitics, part-of-speech, lemma, gloss, and dialect identification. MADARi has a suite of utilities to help with annotator productivity. For example, annotators are provided with pre-computed analyses to assist them in their task and reduce the amount of work needed to complete it. MADARi also allows annotators to query a morphological analyzer for a list of possible analyses in multiple dialects or look up previously submitted analyses. The MADARi management interface enables a lead annotator to easily manage and organize the whole annotation process remotely and concurrently. We describe the motivation, design and implementation of this interface; and we present details from a user study working with this system. Keywords: Arabic, Morphology, Spelling Correction, Annotation 1. Introduction Annotated corpora are vital for research in natural language processing (NLP). These resources provide the necessary training and evaluation data to build automatic annotation systems, and benchmark them. The task of human manual annotation, however, is rather difficult and tedious and sev- eral annotation interface tools have been created to assist in such effort. These tools tend to be specialized to optimize for specific tasks such as spelling correction, part-of-speech (POS) tagging, named-entity tagging, syntactic annotation, etc. Certain languages bring additional challenges to the annotation task. Compared with English, Arabic annotation introduces a need for diacritization of the diacritic-optional orthography, frequent clitic segmentation, and a richer POS tagset. In this paper, we focus on designing and implementing a tool targeting Arabic dialect morphological annotation. Standard Arabic morphology is quite rich (Habash, 2010), but Arabic dialects introduce more complexity than Stan- dard Arabic in that the input text has noisy orthography. For example, the word i. J⌦ mÃ'@AÎÒK.AK⌦ wyAbwhAAlxlyj1 in- volves two spelling errors2 (a word merge and character re- placement) which can be corrected as i. J⌦ mÃ'@ AÎÒK.Ag.  wjAb- whA Alxlyj ‘and they brought it to the Gulf’. Furthermore, the first of the two corrected words includes two clitics that when segmented produce the form: AÎ+ @ÒK.Ag. + w+ jAbwA +hA ‘and+ they-brought +it’. 1Transliterations are in the Habash-Soudi-Buckwalter scheme (Habash et al., 2007). 2Since Arabic dialects do not have a standard orthography, spelling correction here means to conventionalize as per the CODA standard (Habash et al., 2018). Previous work on Arabic morphology annotation inter- faces focused either on the problem of manual annotations for POS tagging (Maamouri et al., 2014), or diacritiza- …

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