Beyond C airo: S a’idi E gyptian A rabic Literary Corpus Construction and Analysis (2025)

Abstract: Egyptian Arabic (EA) NLP resources have mainly focused on Cairene Egyptian Arabic (CEA), leaving sub-dialects like Sa’idi Egyptian Arabic (SEA) underrepresented. This paper introduces the first SEA corpus – an open-source, 4-million-word literary dataset of a dialect spoken by ~30 million Egyptians. To validate its representation, we analyze SEA-specific linguistic features from dialectal surveys, confirming a higher prevalence in our corpus compared to existing EA datasets. Our findings offer insights into SEA’s orthographic representation in morphology, phonology, and lexicon, incorporating CODA* guidelines for normalization.

## introduction dialectal arabic (da) has been a focus of arabic nlp throughout the past few decades, the most advanced da being egyptian arabic (ea) @xcite @xcite @xcite . ea nlp applications and resources primarily feature the most prestigious ea sub-dialect, cairene egyptian arabic (cea), while sub-dialects such as sa'idi egyptian arabic (sea) are marginalized. as representation within the training data (upstream) influences representation within language technology (downstream), lack of da sub-dialect resources impacts the representation of da sub-dialects in arabic nlp @xcite the focus on cea over sea in arabic nlp is not intentionally biased against sea, but motivated by the prominence and high accessibility of cea. sea speakers tend to avoid using marked dialectal features in online writing @xcite , making it challenging to develop representative textual resources. to address this, we target literature, where sea speakers intentionally use their dialect, particularly in sa'idi novels and poetry. this nonface-threatening context allows for the deliberate use of marked features and offers insight into how non-sea speakers perceive sea linguistic production. this paper has three main goals. first, we collect the first sea corpus, a literary dataset of novels, poetry, and short stories, representing a marginalized dialect under-explored in linguistics, literature, digital humanities and nlp. second, we assess sea dialectal feature representation and find that our corpus better reflects sea than naturallyoccurring tweets. these insights guide efforts to integrate sea alongside cea in language technologies and digital humanities research. finally, we present a preliminary study on sea morphological annotation, a key step toward developing analyzers for nlp and digital humanities tasks that require word-form abstractions due to the morphological richness of arabic and sea. 1 2 background and related work modern standard arabic (msa) is the official language of egypt, and egyptian arabic (ea) is the variety spoken among egyptians. while there is a lot of work on msa and on ea (in its cairene variety) @xcite @xcite , we focus here on sea. ## sea literary corpus construction the sea corpus includes poetry and novels. the poetry section features works by sa'idi poets hisham algakh and abdel rahman el-abnudi. while more sa'idi poets exist, many prefer to perform their poetry rather than publish it in books. we selected poets who identify as sa'idi, perform sa'idi poetry, and have published their work in textual form, as we are focused on how sa'idis represent their dialect orthographically. we scan three poetry books from both authors, and use ocr to digitize text from images. we manually correct the ocr digitized text for any erorrs. we plan to include spoken poetry in a future speech corpus. for the novels, we collected works from a self-publishing literary web-forum foot_1 where authors share their novels across 10 genres such as "romantic," "horror," "sa'idi," "true crime," and "science fiction." novels are organized by genre and author, and authors may have contributions in multiple genres, and some novels are written as trilogies. notably, the "sa'idi" genre is the only culturally specific one, reflecting a trend seen in egyptian media, where sa'idi-themed shows and films also exist. novels poetry sea authors 4 2 non-sea authors 22 0 documents 58 novels 355 poems total words 4,541,835 27,170 expected sea words 1,420,998 12,606 we extracted data from the "sa'idi novels" subcategory collecting 58 novels by 26 female authors aged 17-40. the dominance of female writers in novel forums is not atypical from other dialectal varieties, such as the gumar corpus of gulf arabic internet novels @xcite . each novel title follows the same template, making it consistent across the site. this template is "novel title" followed by the number of novels in trilogy "part x" and finally its reference to the author "by author y" -"novel title part x by author y". the first page of each novel includes a descriptive picture with character(s) and the novel title, introduction or sample of the novel, followed by all the linkable chapters. for the introduction, the author includes approximately 1000 words to introduce the synopsis, characters, and settings. on occasions, this section might contain editor notes on novel organization, spelling, or grammatical errors. if the author does not include an introduction, they include a 500 word sample extracted from the novel as a teaser to the novel. for chapter links, every link to each chapter follows a template of "novel title part x by author y chapter z". chapters vary from 1 chapter under the "novella" genre to 56 chapters with an average of 25 chapters per novel. this organizational structure is uniform across all genres, authors, and novels. the stylistic choices of each are mostly consistent by author. for example, if an author uses msa to narrate the novel, they are consistent with using msa in all the novels they write. with 2-3 exceptions, where they use dialectal arabic to narrate the novel once, but msa otherwise. another example is a punctuation signifier for characters beginning their dialogue, where authors are mostly consistent with either ":" or ": -" or a new line. this is is illustrated in we release the corpus organized author by author, and novel by novel. we extract the dialogue only for each chapter using dialogue markers in the novel, and we exclude novels where there is no distinction between dialogue and narration as an attempt to isolate the sea dialect as much as possible from the msa and cea used within the same novel. with this, we achieve our first goal of developing and releasing the first sea corpus. next, we need to understand how representative it is compared to naturally-occurring data as well as examine the written patterns of sea that can further guide sea speech annotation, morphological analysis, and more. ## sea linguistic features to explore sea written production within the corpus, we begin by manual examination of the marked dialectal features of sea presented in the dialogue of the novels and sea poems. we examine a randomized sample of 16,000 words across poetry and prose, while cross-referencing marked dialectal features found in the corpus with the ground-truth dialectal surveys @xcite . results show marked sea features consistent with the groundtruth surveys. cea feature sea feature example ipa letter ipa letter cea sea sea ipa / gloss phonology consonant ɡ ‫ج‬ d ‫د‬ ‫ﺟﻤﻞ‬ ‫دﻣﻞ‬ damal d jml dml camel [d]ʒ ‫ج‬ ‫ﺟﻮزك‬ ‫ﺟﻮزك‬ ʒu:z+ik jwzk jwzk husband + your[2.f.sg] ‫چ‬ ‫ﺟﺮا‬ gra ‫ﭼﺮا‬ jra ʒara happen[pfv.3] ʔ ‫ق/ا‬ a/q ɡ ‫ق/ج‬ j q ‫ﻗﻮي/اوي‬ awy/qwy ‫ﻗﻮي/ﺟﻮي‬ jwy/qwy gawi very vowel lengthening i/a -i:/a: ‫ي‬ ‫ﻛﺪة‬ kdħ ‫اﻛﺪﯾﮫ‬ akdyh ʔikdih like this vowel shortening a:/i: ‫ي‬ a/i -‫ﻣﺮاﺗﻲ‬ mraty ‫ﻣﺮﺗﻲ‬ mrty marat+i wife+my ipa morpheme ipa morpheme cea sea sea ipa / gloss morphology future prefix h, ħ ‫ه،ح-‬ ʕ,ħ ‫ع،ح-‬ ‫ھﺘﺴﻜﻦ‬ htskn ‫ﻋﺘﺴﻜﻦ‬ ςtskn ʕa+t+skun will+you[2.sg.masc]+live[impfv] negation ma-ʃ ‫م-ش‬ ma-ʃ ‫م-ﺷﻲ‬ ‫ھﺘﺤﺮك‬ ‫ﻣﺶ‬ mš hthrk ‫ﻣﮭﺘﺤﺮﻛﺸﻲ‬ mhthrkšy ma+h+at+ħark+ʃi not+will+move[1.impfv]+not -ʃ ‫م-ش‬ ‫ﻣﺠﺎش‬ mgaš ‫ﻣﺠﺎش‬ mgaš ma+ʒa:+ʃ not+come[3.sg.masc]+not -ʃi ‫ﺷﻲ‬ ‫ﻣﺮوﺣﺘﻮﻟﮭﺎش‬ mrwhtwlhaš ‫روﺣﺘﻮﻟﮭﺎﺷﻲ‬ rwhtwlhašy ruħt+tu+l+ha:+ʃi go[3.impfv]+you[pl]+to+her+not 1.p.s. prefix-suffix ʔ ‫أ-‬ n-u: ‫ن-وا‬ ‫أروح‬ ârwh ‫ﻧﺮوﺣﻮا‬ nrwhwa n+ru:ħ+u: [1.sg]+go[1.sg.impfv] ## towards a morphologically annotated sea corpus in this section, we present a preliminary study partially automating sea morphological annotation using existing ea morphological analysis tools to streamline sea morphological analysis and annotation. following the methodology in @xcite and @xcite by using coda* @xcite and calima egy @xcite , we present a semiautomated morphological annotation process for sea, with expected modifications and results. ## conclusions and future work this paper presents the first sea corpus, including its construction and analysis of sea representation. we find the corpus moderately representative of sea, though its consistency across authors is influenced by variation within sea sub-dialects @xcite . despite this, the corpus offers valuable insight into the phonological, orthographic, lexical, morphological, and variations between sea and cea. future work will focus on expanding the sea corpus with additional spoken and textual content, as well as manual annotations to improve its consistency, representation, and overall usability. we also plan to develop automatic tools for processing sea to support broader linguistic research and application development.