# Automatic Era Identification in Classical Arabic Poetry
## 6 Conclusions and Future Work  
In this study, we explored the possibility of automatically identifying the era when a poem was written, which is a real practical challenge when considering pre- and early Islamic poems through to the end of the Umayyad period. We presented classification results using a variety of machine learning methods, including the generative Naive Bayes, example-based KNN, discriminative decision trees, margin-based SVM, and the perceptron, which is a one-layer neural network. These methods are adept at processing datasets with a large number of features effectively. In our experiments, we demonstrated the possibility of achieving a high level of accuracy in era identification using the SVM-SMO classifier. Further experiments demonstrated the importance of all of the features we used. While lexical (word and character level) features were dominant in their contribution to the final results, we found that the topics included in the poem and the rhetorical elements in the poem, as well as general statistics about the poem length and structure, are informative for era classification. Importantly, we also reported the results of a strict evaluation setup, where the classifier is applied across poets, and showed robustness of the classification model in this setup. In other words, classification of the poems' eras generalizes across poets, and is expected to also give good results for poems by poets that were not included in the training set. These results have practical importance for researchers of pre-Islamic and early Islamic (including Umayyad) poetry, given the high uncertainty about the authorship of poems attributed to these eras. In our experiments, fifteen out of 198 poems that are attributed to the 'Abbasid era (7.5%) were classified as pre-Abbasid. Considering a finer era resolution,we observed that thirteen poems attributed to the Umayyad period were automatically classified as 'Abbasid and 35 as pre-Islamic. Four poems were classified as belonging to the 'Abbasid period and thirty-two as belonging to the Umayyad period, while being attributed to poets of the pre-Islamic period. The classifier is not free of errors; however, it compiles various multiples of evidence into an era prediction, showing high performance. Classification models also provide a confidence score, indicating the strength of that evidence. Overall, these results may help the research community in focusing its attention on poems for which there is a high chance of attribution errors or fallacy. As noted above, the dataset we had was small and imbalanced. It is possible for researchers to model additional conjectures or intuitions as additional features and test the contribution of those features using our framework. As demonstrated, our analysis provides visibility to topic distributions, as well as the representation of the poems with respect to the other features, rather individually or collectively. Future research should try to address the more challenging (and important) task of authorship attribution of poems in these eras. To support this, we will try to work with a larger, more balanced dataset and, if needed, consider techniques that will enable us to apply down- or up-sampling to enlarge the dataset. Moreover, as technology continues to evolve, it may also be worth considering state-of-the-art language processing tools like CaMeL Tools and its recent classical Arabic pre-trained models ([PERSON] et al. 2021). When considering adapting AraPoetryBERT ([PERSON] et al. 2021) or similar models to era and poet classification, high results may be achieved by finetuning the model using labeled examples. Nevertheless, we note that the modeling of the various feature types that are implemented in this work, including the elements that are characteristic of classical Arabic poetry, may be non-trivial using LLMs. In addition, LLMs are a black box, in that feature modeling is latent and obscure, and there are no explanations for the models' decisions.