# Automatic Era Identification in Classical Arabic Poetry
## 1 Introduction
However, modern scholars argue that, in its early stages, Islam did not significantly influence poetry [6]. The poetry of the _mubadram_s, with a few exceptions (see, for example, [PERSON] [5]), often more closely resembled pre-Islamic poetry than Umayyad poetry. Therefore, we decided to conduct classification experiments targeting a distinction between three periods, combining the poems of the pre-Islamic and _mubadram_ periods into a single category. It would be interesting for future research to study the precise characteristics of the poetry composed by the _mubadram_ poets, drawing on a larger corpus that includes all extant poems attributed to poets from that period. Such research could investigate which poems were indistinguishable from pre-Islamic poetry and which exhibited early signs of change. In learning to classify the writing period of each poem, we encoded each poem using a variety of descriptive features that, we believe, are characteristic of pre-Islamic poetry. Overall, we evaluate and report the contribution of five types of descriptive features:statistical (e.g., poem length), lexical (e.g., word distribution), topical, syntactic, and stylistic features that are attributed to classical Arabic poetry. We show that each of the examined feature types is informative. We achieve high quality results, measuring between 0.73-0.90 in terms of F1 per the various target periods (see an explanation about this evaluation metric below). The research question of era prediction is closely related to the well-studied task of authorship attribution (AA), in which the goal is to automatically identify the author of a given text. Some previous work on authorship attribution has included texts written in Arabic [7]. While lexical features that describe the author's word choices are commonly used in studies of text classification and AA, the remaining feature types, mainly the domain features and topic analysis, introduce a unique contribution of this study. A related study, published in various venues, used a dataset of 114 poets from different eras and examined authorship attribution in these poems by considering key features such as character count, sentence length, word length, rhyme, meter, and the first word of the sentence [8; 9; 10]. This study, however, does not disclose which poets were examined or their time periods. In this work, we rather rely on a larger corpus of poems with established periodization. There exist several studies that specifically addressed the classification of Arabic poems according to the eras in which they were written. These include the study by [PERSON] et al. [11], which applied machine learning techniques to predict the authorship period of poems, focusing on the pre-Islamic, Islamic, and 'Abbasid periods. Another related study by [PERSON] [12] achieved an accuracy of 70.21% and an F1 score of 68.8% on this task. We believe that our study is more comprehensive, building upon and expanding those previous research efforts. We propose and study a novel set of stylistic features such as the use of synonyms,'same but different', and 'duplicate _kaffya_' (rhyming word--see Section 4 for a complete list of features and their explanations). In addition, we combined the morphological analysis tools of MADA and Tokan (see Section 2.4), where the resulting rich morphological analysis enables a deeper understanding of word meanings within their context. In terms of experimental setup, we explored a diverse set of learning methods, reported a feature ablation study, and examined the generalization of the models using a rigorous evaluation setup, having era prediction models trained and tested on poems authored by a different set of poets, such that the models are restricted to modeling eras rather than individual poets' styles. Overall, this work presents higher classification performance compared with previous related efforts.