Camelira: An A rabic Multi-Dialect Morphological Disambiguator (2022)

Abstract: We present Camelira, a web-based Arabic multi-dialect morphological disambiguation tool that covers four major variants of Arabic: Modern Standard Arabic, Egyptian, Gulf, and Levantine.Camelira offers a user-friendly web interface that allows researchers and language learners to explore various linguistic information, such as part-of-speech, morphological features, and lemmas. Our system also provides an option to automatically choose an appropriate dialect-specific disambiguator based on the prediction of a dialect identification component. Camelira is publicly accessible at http://camelira.camel-lab.com .

## introduction the last two decades have witnessed remarkable progress in natural language processing (nlp) for arabic and its dialects despite many challenges such as its diglossic nature, morphological complexity, and orthographic ambiguity @xcite . these efforts have led to many practical applications for various nlp tasks including tokenization, part-of-speech (pos) tagging, morphological disambiguation, named entity recognition, dialect identification (did), and sentiment analysis @xcite @xcite . tools for core technologies like pos tagging and morphological disambiguation are primary examples of such successful applications, e.g., madamira @xcite , farasa @xcite , udpipe @xcite , and stanza @xcite . however, there are still gaps to be filled in terms of coverage and usability. for example, these systems only support modern standard arabic (msa) and egyptian arabic, but not other widely spoken dialects such as gulf and levantine. in addition, these web interfaces only present the top prediction, although the alternative readings could provide valuable information for analyzing the models' behavior. in contrast, morphological analyzers such as elixirfm @xcite , calima star @xcite , calima egyptian @xcite show all the different readings for a given word out of context but without disambiguated analyses in context. these tools assume that users already know the input did; however, this is not necessarily the case for second language learners. to address these limitations, we present camelira, foot_0 , foot_1 a web interface for arabic multidialect morphological disambiguation that covers four major variants of arabic: msa, egyptian, gulf, and levantine. our system takes an input sentence and provides automatically disambiguated readings for each word in context, as well as its alternative out-of-context readings. we also showcase the integration of a state-of-the-art morphological disambiguator @xcite with the highest performing fine-grained arabic did system @xcite on the madar did shared task @xcite . camelira provides an option to automatically choose a dialectspecific disambiguator based on the prediction of the did component. to the best of our knowledge, our work is the first to demonstrate an integrated web application that leverages both arabic morphological disambiguation and did systems. our contributions are as follows: (a) we present a user-friendly web interface that allows researchers and language learners to explore the detailed linguistic analysis of a given arabic sentence. (b) we include three major arabic dialects (egyptian, gulf, and levantine) in addition to msa, to make our tool more accessible to a wider audience. (c) we integrate did to automatically select the appropriate disambiguator; a feature that helps users with limited knowledge of arabic dialects. ## arabic linguistic facts the arabic language poses a number of challenges for nlp @xcite . we highlight three aspects that are most relevant to multi-dialectal morphological modeling: dialectal variations, morphological richness, and orthographic ambiguity. first, arabic is characterized with diglossia and its large number of dialects @xcite . msa is the shared standard variant used in official contexts, while the dialects are the varieties of daily use. msa and the dialects vary among themselves in different aspects, such as lexicons, morphology, and syntax. second, arabic is a morphologically rich and complex language. it employs a combination of templatic, affixational, and cliticization morphological operations to represent numerous grammatical features such as gender, number, person, case, state, mood, aspect, and voice, in addition to a number of attachable pronominal, preposition, and determiner clitics. third, arabic is orthographically highly ambiguous. this is due to its orthographic conventions where diacritical marks are often omitted, leading to a high degree of ambiguity. for example, msa can have 12 different morphological analyses per word on average @xcite . ## conclusion and future work we presented camelira, a user-friendly web interface for arabic multi-dialect morphological disambiguation that covers four major variants of arabic. the system takes a sentence as input and provides an automatically disambiguated reading for each word, as well as its alternative readings, allowing users to explore various linguistic information, such as part-of-speech, morphological features, and lemmas. camelira also provides an option to automatically choose an appropriate dialect-specific disambiguator based on the prediction of its dialect identification component. in the future, we plan to extend our disambiguation system to cover other arabic dialects such as maghrebi and yemeni arabic. we also plan to continue to update the system using future improvements in terms of efficiency and accuracy in camel tools @xcite .