# Automatic Gender Identification from Text
## 5 Case Study  
To assess the ability of our classifiers to identify female authors who wrote under male pen names, we conducted an experiment using 12 books from the BookSCE dataset. These books were authored by females using male pseudonyms and are listed in Table 3. For this experiment, we used a different data split: the training set included all books from the BookSCE dataset except those listed in Table 3, while the test set comprised only the books listed in Table 3. For the experiment, we applied three classifiers: GPT2, XLNet, and LR. The results showed that GPT2 achieved an accuracy of 0.833, with XLNet accuracy of 0.750. However, the LR model did not perform well for this task, achieving only 0.333 (Table 3). It is worth noting the complex and deeply layered novel \"_Middlement_\", which was renowned for its exploration of social, political, and personal issues in 19 th-century England. [PERSON]'s narrative style in this book is intricate, with a focus on psychological depth and realism, which might mirror the writing styles of male authors of the time, which may explain why female recognition was more challenging. \"[PERSON]\" focuses heavily on a male character, making it somewhat difficult for classifiers to correctly identify the author's gender. We also assume that numerous short stories compiling \"Impressions of Theophrastus Such\" make the inference task to one gender complicated.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Model** & **Acc (avg)** & **Precision\({}_{f}\)** & **Precision\({}_{m}\)** & **Recall\({}_{f}\)** & **Recall\({}_{m}\)** & **F1\({}_{f}\)** & **F1\({}_{m}\)** & **F1 (avg)** \\ \hline LR & 0.837 & 0.605 & 0.886 & 0.528 & 0.914 & 0.564 & 0.900 & 0.732 \\ LR\({}_{L2}\) & 0.841 & 0.637 & 0.889 & 0.576 & 0.912 & 0.605 & 0.901 & 0.753 \\ LR\({}_{L1}\) & 0.825 & 0.601 & 0.870 & 0.485 & 0.915 & 0.537 & 0.892 & 0.714 \\ XGB & 0.853 & 0.787 & 0.861 & 0.402 & 0.971 & 0.532 & 0.913 & 0.723 \\ SVM & 0.809 & 0.548 & 0.878 & 0.538 & 0.882 & 0.543 & 0.880 & 0.711 \\ BERT & 0.739 & 0.286 & 0.800 & 0.160 & 0.894 & 0.205 & 0.844 & 0.525 \\ GPT2 & **0.925** & **0.851** & **0.944** & **0.784** & **0.963** & **0.816** & **0.953** & **0.885** \\ XLNET & 0.907 & 0.806 & 0.931 & 0.735 & 0.953 & 0.769 & 0.942 & 0.856 \\ RoBERTa & 0.711 & 0.140 & 0.779 & 0.070 & 0.884 & 0.093 & 0.828 & 0.461 \\ T5 & 0.778 & 0.210 & 0.790 & 0.021 & 0.978 & 0.039 & 0.874 & 0.456 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model performances on the BookSCE dataset; the \(f\) and \(m\) subscripts indicate that the corresponding value is computed for the female (\(f\)) or male (\(m\)) class.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{3}{c}{**Book Information**} & \multicolumn{3}{c}{**Female Gender Recognition**} \\ \hline
**Author** & **Year** & **Book Name** & **GPT2** & **XLNet** & **LR** \\ \hline [PERSON] & 1832 & _Middlement_ & X & X & X \\ \hline [PERSON] [PERSON] & 1859 & _[PERSON]_ & V & V & X \\ \hline [PERSON], [PERSON] & 1861 & _[PERSON]_ & V & V & X \\ \hline [PERSON], [PERSON] & 1860 & _The Mill on the Floss_ & V & V & V \\ \hline [PERSON] [PERSON] & 1876 & _[PERSON]_ & V & X & X \\ \hline [PERSON] [PERSON] & 1879 & _Impressions of [PERSON]_ & X & V & X \\ \hline [PERSON], [PERSON] & 1857 & _Scenes of Clerical Life_ & V & V & V \\ \hline [PERSON], [PERSON] & 1862 & _[PERSON]_ & V & V & X \\ \hline [PERSON], [PERSON] & 1866 & _[PERSON]_ & V & V & X \\ \hline [PERSON] & 1886 & _A Phantom Lover_ & Yes & V & V \\ \hline [PERSON] & 1903 & _Penelope Handling: A Tale of the Welsh Coast in the Eighteenth Century_ & V & V & X \\ \hline [PERSON] & 1848 & _The Tchant of Wildelfl Hall_ & V & X & V \\ \hline \hline \end{tabular}
\begin{tabular}{l c c c c} \hline \hline
**Acc** & 0.833 & 0.750 & 0.333 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Books by female authors using male pen names from the BookSCE dataset. Note: V indicates “Correct” and **X** indicates “Not Correct” recognition.