# Automatic Era Identification in Classical Arabic Poetry
## 5 Discussion  
This study focused on era identification of classical Arabic poetry. We distinguished between two main eras, namely, pre-'Abbasid and 'Abbasid, and evaluated several classifiers on the task of era identification of poems that are attributed to poets of those eras with high certainty. We experimented with several different classifiers which have been successfully used for Arabic text categorization tasks over the years. Our study found SVM to perform best on the era attribution task, yielding a high classification performance of 0.887 in terms of F1. Once the most successful classifier was identified, additional experiments were performed, starting with an attempt to achieve a more fine-grained era identification. The results of this more challenging task were almost as successful as the first experiment. This was followed by ablation experiments aimed at exploring the contribution of the different features to the overall result. It was found that all of the feature types contribute to the overall success; however, the lexical features seem to have the highest impact on performance once removed from the feature set. We conjecture that the impact of removing the feature types was minor due to overlaps with the lexical features. Evaluating the feature types in isolation revealed, however, that all of the feature types are informative, yielding a classification performance that is better than a random guess. This indicates that encoding topics and stylistic elements that characterize the specific domain of classical Arabic poetry captures era-related phenomena. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & ‘**Abbásid** & **Umayyad** & **Pre-Islamic *** \\ \hline \hline
**Abbasid** & **1.0** & **0.0** & **0.0** \\ \hline
**Umayyad** & 13.0 & 83.0 & 35.0 \\
**Pre-Islamic (including _mubadram_ poets)\_** & 4.0 & 32.0 & 31.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Confusion matrix for three-period categorization. (* The pre-Islamic period includes the _mubadram_ poets). \begin{table}
\begin{tabular}{c c c} \hline \hline  & ‘**Abbasid** & **Pre-** **Abbasid** \\ \hline \hline
**Abbasid** & **1.0** & **0.0** \\ \hline
**Pre-** **Abbasid** & 15.0 & 183.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Confusion matrix for two-period categorization. Another interesting result was obtained when we removed all of the poems by an individual poet completely from the training set and tested the classifier on predicting the era of each of the poems by this poet. In this setup, we ensured that the classifier did not leverage similarities within the poems of the poet, and simulated a setup in which the model is applied to poems by a poet that was not included in our dataset. The overall results were very high. The practical meaning of this result is that regardless of the poet and the poet's location, poems written in a specific era have something in common that differentiates them from poems written in a different era. Finally, we applied the best classifier to the small set of 'attributed' poems, where the results suggested that some of them may be attributed to the wrong era. This interesting result demonstrates the potential contribution of our approach and calls for further research by domain experts. As any study, this study has limitations. The first limitation is the small size of the annotated dataset. The dataset contained 3618 poems, attributed to thirteen poets. The second limitation is the imbalance of poets within the dataset, which prevented us from experimenting with the more challenging task of authorship attribution; for example, some poets were represented by 900 poems and others by just 35. In future research, it is recommended that the dataset be extended with more poems as well as considering the possibility of applying up-sampling techniques to achieve a more balanced label distribution for learning purposes. Another limitation is that among the poems we considered, there were short poems only a few lines long.