# PaperNet: A Dataset and Benchmark for Fine-Grained Paper Classification
## Appendix A  
Some detailed experiment results. \begin{table}
\begin{tabular}{c l c c c} \hline \hline
**Dataset** & **Class** & **Precision** & **Recall** & **F1-Score** \\ \hline PaperNet\_2 & CV & 0.96 & 0.96 & 0.96 \\ NLP & 0.97 & 0.97 & 0.97 \\ \hline PaperNet\_20 & CV\_attention & 0.83 & 0.85 & 0.84 \\ CV\_classification & 0.93 & 0.84 & 0.88 \\ CV\_detection & 0.93 & 0.97 & 0.95 \\ CV\_GAN & 0.73 & 0.60 & 0.66 \\ CV\_recognition & 0.95 & 0.90 & 0.92 \\ CV\_retrieval & 0.95 & 0.90 & 0.92 \\ CV\_segmentation & 0.92 & 0.87 & 0.89 \\ NLP\_bert & 0.82 & 0.80 & 0.81 \\ NLP\_conversation & 0.81 & 0.85 & 0.83 \\ NLP\_cross & 0.76 & 0.79 & 0.77 \\ NLP\_extraction & 0.78 & 0.82 & 0.80 \\ NLP\_Few\_shot & 0.59 & 0.67 & 0.63 \\ NLP\_knowledge\_graph & 0.54 & 0.70 & 0.61 \\ NLP\_machine\_reading & 0.91 & 0.50 & 0.65 \\ NLP\_machine\_translation & 0.86 & 0.86 & 0.86 \\ NLP\_multilingual & 0.80 & 0.82 & 0.81 \\ NLP\_multimodal & 0.75 & 0.70 & 0.72 \\ NLP\_named\_entity\_recognition & 0.74 & 0.85 & 0.79 \\ NLP\_sentiment\_analysis & 0.59 & 0.74 & 0.66 \\ NLP\_text\_generation & 0.68 & 0.65 & 0.67 \\ \hline PaperNet\_CV & CV\_attention & 0.52 & 0.99 & 0.68 \\ CV\_classification & 0.99 & 0.81 & 0.90 \\ CV\_detection & 0.92 & 0.98 & 0.95 \\ CV\_GAN & 0.50 & 0.49 & 0.50 \\ CV\_recognition & 0.99 & 0.05 & 0.10 \\ CV\_retrieval & 0.51 & 0.50 & 0.50 \\ CV\_segmentation & 0.88 & 0.99 & 0.94 \\ \hline PaperNet\_NLP & NLP\_bert & 0.87 & 0.85 & 0.86 \\ NLP\_conversation & 0.96 & 0.83 & 0.89 \\ NLP\_cross & 0.87 & 0.93 & 0.90 \\ NLP\_extraction & 0.83 & 0.85 & 0.84 \\ NLP\_Few\_shot & 0.69 & 0.75 & 0.72 \\ NLP\_knowledge\_graph & 0.68 & 0.75 & 0.71 \\ NLP\_machine\_reading & 0.56 & 0.45 & 0.50 \\ NLP\_machine\_translation & 0.92 & 0.90 & 0.91 \\ NLP\_multilingual & 0.84 & 0.84 & 0.84 \\ NLP\_multimodal & 0.93 & 0.93 & 0.93 \\ NLP\_named\_entity\_recognition & 0.75 & 0.90 & 0.82 \\ NLP\_sentiment\_analysis & 0.76 & 0.89 & 0.82 \\ NLP\_text\_generation & 0.93 & 0.70 & 0.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Detailed results of the Adaboost algorithm. \begin{table}
\begin{tabular}{c l c c c} \hline \hline
**Dataset** & **Class** & **Precision** & **Recall** & **F1-Score** \\ \hline PaperNet\_2 & CV & 0.98 & 0.94 & 0.96 \\ NLP & 0.96 & 0.98 & 0.97 \\ \hline PaperNet\_20 & CV\_attention & 0.24 & 0.23 & 0.23 \\ CV\_classification & 0.44 & 0.44 & 0.44 \\ CV\_detection & 0.57 & 0.75 & 0.65 \\ CV\_GAN & 0.44 & 0.60 & 0.50 \\ CV\_recognition & 0.62 & 0.55 & 0.58 \\ CV\_retrieval & 0.38 & 0.15 & 0.21 \\ CV\_segmentation & 0.54 & 0.40 & 0.46 \\ NLP\_bert & 0.62 & 0.64 & 0.63 \\ NLP\_conversation & 0.71 & 0.79 & 0.75 \\ NLP\_cross & 0.49 & 0.52 & 0.50 \\ NLP\_extraction & 0.68 & 0.60 & 0.64 \\ NLP\_Few\_shot & 0.48 & 0.46 & 0.47 \\ NLP\_knowledge\_graph & 0.88 & 0.75 & 0.81 \\ NLP\_machine\_reading & 0.99 & 0.50 & 0.67 \\ NLP\_machine\_translation & 0.73 & 0.85 & 0.79 \\ NLP\_multilingual & 0.66 & 0.54 & 0.59 \\ NLP\_multimodal & 0.77 & 0.67 & 0.71 \\ NLP\_named\_entity\_recognition & 0.80 & 0.60 & 0.69 \\ NLP\_sentiment\_analysis & 0.57 & 0.60 & 0.58 \\ NLP\_text\_generation & 0.92 & 0.55 & 0.69 \\ \hline PaperNet\_CV & CV\_attention & 0.45 & 0.38 & 0.41 \\ CV\_classification & 0.53 & 0.47 & 0.50 \\ CV\_detection & 0.56 & 0.75 & 0.64 \\ CV\_GAN & 0.48 & 0.58 & 0.53 \\ CV\_recognition & 0.64 & 0.48 & 0.55 \\ CV\_retrieval & 0.70 & 0.35 & 0.47 \\ CV\_segmentation & 0.55 & 0.44 & 0.49 \\ \hline PaperNet\_NLP & NLP\_bert & 0.60 & 0.62 & 0.61 \\ NLP\_conversation & 0.67 & 0.73 & 0.70 \\ NLP\_cross & 0.53 & 0.55 & 0.54 \\ NLP\_extraction & 0.71 & 0.66 & 0.69 \\ NLP\_Few\_shot & 0.45 & 0.58 & 0.51 \\ NLP\_knowledge\_graph & 0.89 & 0.80 & 0.84 \\ NLP\_machine\_reading & 0.99 & 0.50 & 0.67 \\ NLP\_machine\_translation & 0.72 & 0.83 & 0.77 \\ NLP\_multilingual & 0.57 & 0.50 & 0.53 \\ NLP\_multimodal & 0.82 & 0.90 & 0.86 \\ NLP\_named\_entity\_recognition & 0.80 & 0.60 & 0.69 \\ NLP\_sentiment\_analysis & 0.64 & 0.66 & 0.65 \\ NLP\_text\_generation & 0.90 & 0.45 & 0.60 \\ \hline \hline \end{tabular}
\end{table}