# Automatic Era Identification in Classical Arabic Poetry
## 4 Experiments and Results
### Experiment 5: Applying the Era Classification Model to the Attributed and Poorly Attributed Poems  
Next, having trained and evaluated the era classification models, we applied classification so as to confirm the era that is attributed to the attributed and poorly attributed poems in our dataset. In this experiment, we employed the best-performing classifier evaluated thus far, the SVM (SMO). Rather than perform CV experiments, we trained a model using all of the 'original' poems in our dataset, 3386 poems, roughly equally distributed over the two periods. We then applied the classifier to those poems whose ascription to poets is not certain. Overall, there is one poem attributed to poets in the 'Abbasid era and 198 poems that are attributed to poets in the pre-' Abbasid era in our dataset whose authorship is not fully acknowledged.  
Table 9 shows our results in a confusion matrix. As seen, the vast majority of poems is classified into the era of the attributed poets of the poems under dispute. Fifteen poems,  
Figure 4: Results of Experiment 4—leave one poet out.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **P** & **R** & **F1** & **Num. of Poems** & **Era** \\ \hline Abq 1-[PERSON] & 1.000 & 0.900 & 0.940 & 200 & ‘[PERSON] \\ [PERSON] b. [PERSON] & 1.000 & 0.670 & 0.810 & 684 & Pre-’Abbasid \\ [PERSON] & 1.000 & 0.750 & 0.850 & 173 & ‘[PERSON] \\
**[PERSON]** & 1.000 & 0.840 & 0.910 & 204 & Pre-’Abbasid \\ [PERSON] & 1.000 & 0.20 & 0.760 & 94 & Pre-’Abbasid \\ [PERSON] & 1.000 & 0.730 & 0.840 & 408 & ‘[PERSON] \\ [PERSON] & 1.000 & 0.760 & 0.860 & 903 & ‘[PERSON] \\ [PERSON] & 1.000 & 0.580 & 0.730 & 265 & Pre-’Abbasid \\ [PERSON] & 1.000 & 0.980 & 0.990 & 35 & Pre-’Abbasid \\ [PERSON] & 1.000 & 0.530 & 0.690 & 185 & Pre-’Abbasid \\ [PERSON] & 1.000 & 0.910 & 0.960 & 140 & Pre-’Abbasid \\ [PERSON] b. [PERSON] & 1.000 & 0.920 & 0.960 & 64 & Pre-’Abbasid \\ [PERSON] b. [PERSON] & 1.000 & 0.760 & 0.860 & 25 & Pre-’Abbasid \\
**Average** & 1.000 & 0.765 & 0.858 & & \\ ‘Abbasid- Average & 1.000 & 0.785 & 0.873 & & \\ Pre-‘Abbasid Average & 1.000 & 0.757 & 0.852 & & \\
**Weighted average** & 1.000 & 0.732 & 0.840 & & \\ ‘Abbasid W.AVG & 1.000 & 0.768 & 0.864 & & \\ Pre-‘Abbasid W.AVG & 1.000 & 0.695 & 0.816 & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of Experiment 4—leave one poet out.  
however, were classified into a different era than what is assumed, attributed to the pre-'Abbasid instead of the 'Abbasid era. We hope that this finding will assist the research community in focusing its attention on poems for which there is a high chance of poet- and era-attribution errors or fallacy.  
Experiment 6: Applying the Era Classification Model to the Attributed and Poorly Attributed Poems Considering a Finer Resolution: The 'Abbasid, Ulmayyad, and Pre-Islamic Periods  
Finally, we repeated Experiment 6 using a three-period categorization. The training data again contained 3386 poems from the three periods. The test set contained one poem attributed to the 'Abbasid era, 131 poems attributed to the Umayyad period, and 67 poems attributed to the pre-Islamic period. The classification results are presented in Table 10 as a confusion matrix.  
We observe that 13 poems, actually 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 32 as Umayyad period, while being attributed to poets of the pre-Islamic period. Overall, our predictions disagree with the era attributed to 84 poems, which may be of interest to the research community.