Computational Morphology and Lexicography Modeling of M odern S tandard A rabic Nominals (2024)

Abstract: Modern Standard Arabic (MSA) nominals present many morphological and lexical modeling challenges that have not been consistently addressed previously. This paper attempts to define the space of such challenges, and leverage a recently proposed morphological framework to build a comprehensive and extensible model for MSA nominals. Our model design addresses the nominals’ intricate morphotactics, as well as their paradigmatic irregularities. Our implementation showcases enhanced accuracy and consistency compared to a commonly used MSA morphological analyzer and generator. We make our models publicly available.

## introduction arabic poses many challenges to computational morphology: its hybrid templatic and concatenative processes, rich collections of inflectional and cliticization features, numerous allomorphs, and highly ambiguous orthography. over the decades, many approaches have been explored in developing arabic morphological analyzers and generators @xcite @xcite @xcite . these tools continue to show value for arabic natural language processing (nlp) even when paired with state-of-the-art neural models on various tasks such as morphological tagging @xcite , sentiment analysis @xcite , and controlled text rewriting @xcite . developing such tools is neither cheap nor easy; and some of them are not freely available, or incomplete, e.g., @xcite points out how a popular arabic analyzer, sama @xcite , has very low coverage for phenomena such as command form or passive voice. the effort presented in this paper is about the modeling of modern standard arabic (msa) nominals in an open-source arabic morphology project (camelmorph) introduced by @xcite , who demonstrated their approach on verbs in msa and egyptian arabic. verbs are generally seen as the sweethearts of arabic computational morphology: while they have some complexity, they are very regular and predictable. nominals are far more complex -in addition to their numerous morphotactics, they have complicated paradigms with different degrees of completeness and many irregular forms, e.g., broken plural and irregular feminines @xcite . our contributions are (a) defining the space of challenges in modeling msa nominals (nouns, adjectives, and elatives/comparative adjectives); (b) developing a large-scale implementation which is easily extendable within the recently introduced camelmorph framework; (c) benchmarking our models against a popular arabic morphology database @xcite and demonstrating them to be more accurate and consistent; and finally (d) making our databases and code publicly available. 1 next, we present relevant terminology ( §2), and related work ( §3). we follow with a discussion of arabic nominal modeling challenges ( §4), and give an overview on the camelmorph framework ( §5) and how we utilize it ( §6). finally, we present an evaluation of our system ( §7). ## relevant terminology we present the relevant terminology we use in this paper and illustrate it with examples in finally, appendix a presents a glossary of the discussed terms, with their abbreviations, foot_1 arabic equivalents, and examples. ## arabic nominal morphology default word composition assumes a straightforward one-to-one mapping from features to morphemes, with simple interdigitation and concatenation. in practice, however, there are many variations and exceptions. we outline the most important issues next, starting with word-level inflection and cliticization, and following with lexicographic and paradigmatic challenges. ## evaluation we assess the quality of our system by (a) evaluating its coverage of the training portion of the penn arabic treebank (patb; latest versions of parts 1,2,3) @xcite as defined by @xcite , and (b) comparing the analyses it generates with those of calima db over a list of specific words. ## conclusion and future work we presented a detailed review of the challenges of modeling arabic nominals morphologically and lexically. we developed an annotator-friendly and easily extendable system for modeling nouns, adjectives and comparative adjectives building on an existing open-source framework for arabic morphology. we evaluated our system against a popular analyzer for arabic, showing that our resulting database is more consistent and provides a more accurate linguistic representation. we make our models, system details, and guidelines publicly available (see footnote 1). in the future, we plan to extend our work to other msa pos tags and to arabic dialects. we also plan to make our model more robust to spelling variations and integrate it in downstream applications, e.g., morphological disambiguation, tokenization and diacritization @xcite , readability visualization @xcite , gender rewriting @xcite , error typing @xcite , and grammatical error correction @xcite . 1079 the current system faces several limitations: it lacks robustness in handling input orthographic errors, restricting its usability in spontaneous orthography contexts. additionally, it does not comprehensively model valid spelling variants commonly used. the high coverage generates numerous options, including some less likely but theoretically correct ones, potentially overwhelming downstream processes without optimized filtering and ranking models. there is also a lack of explicit linking across lemmas sharing derivational history. furthermore, the model is currently limited to nouns, adjectives, and comparative adjectives, representing the open-class nominals at this stage.