# Background Knowledge Based Multi-Stream Neural Network for Text Classification
## 4 Experiments and Discussion
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multirow{2}{*}{**Unit \%**} & \multicolumn{4}{c}{**Macro**} & \multirow{2}{*}{**Acc**} \\ \cline{3-4}  & & **P** & **R** & **F1** & \\ \hline \multirow{3}{*}{Single stream:} & **W (Baseline)** & **76.70** & **76.41** & **76.55** & **95.43** \\ \cline{2-4}  & Key & 78.16 & 68.12 & 72.79 & 90.82 \\ \cline{2-4}  & Cow & 83.27 & 78.15 & 80.63 & 95.22 \\ \cline{2-4}  & KeyCow & 77.84 & 74.05 & 75.90 & 94.52 \\ \hline \multirow{3}{*}{Early fusion:} & W + Key & 83.82 & 79.63 & 81.67 & 96.15 \\ \cline{2-4}  & W + Co & 84.44 & **81.73** & 83.06 & **96.79** \\ \cline{1-1} \cline{2-4}  & W + KeyCow & 83.02 & 80.80 & 81.90 & 96.53 \\ \cline{1-1} \cline{2-4}  & W + Key +Cow & **88.69** & **81.65** & **85.03** & **96.89** \\ \hline \hline \end{tabular} \begin{tabular}{c c c c c} \hline \hline  & \multirow{2}{*}{Average pooling} & \multirow{2}{*}{\begin{tabular}{c} W + Key \\ W + Co \\ W + KeyCow \\ W + KeyCow \\ W + KeyCow \\ W + KeyCow \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} 84.18 \\ 84.69 \\ 83.90 \\ 86.62 \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} 78.99 \\ 80.96 \\ 81.19 \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} 81.50 \\ 96.48 \\ 81.19 \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} 95.98 \\ 96.48 \\ 96.13 \\ \end{tabular} } \\ \cline{2-4}  & W + Key +Cow \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of Fudan Corpus. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multirow{2}{*}{**Unit \%**} & \multicolumn{4}{c}{**Macro**} & \multirow{2}{*}{**Acc**} \\ \cline{3-4}  & & **P** & **R** & **F1** \\ \cline{3-4}  & & **W (Baseline)** & **76.70** & **76.41** & **76.55** & **95.43** \\ \cline{2-4}  & Key & 78.16 & 68.12 & 72.79 & 90.82 \\ \cline{2-4}  & Cow & 83.27 & 78.15 & 80.63 & 95.22 \\ \cline{2-4}  & KeyCow & 77.84 & 74.05 & 75.90 & 94.52 \\ \hline \multirow{3}{*}{Early fusion:} & W + Key & 83.82 & 79.63 & 81.67 & 96.15 \\ \cline{2-4}  & W + Co & 84.44 & **81.73** & 83.06 & **96.79** \\ \cline{1-1} \cline{2-4}  & W + KeyCow & 83.02 & 80.80 & 81.90 & 96.53 \\ \cline{1-1} \cline{2-4}  & W + Key +Cow & **88.69** & **81.65** & **85.03** & **96.89** \\ \hline \hline \end{tabular} \begin{tabular}{c c c c c} \hline \hline  & \multirow{2}{*}{Average pooling} & \multirow{2}{*}{\begin{tabular}{c} W + Key \\ W + Co \\ W + KeyCow \\ W + KeyCow \\ W + KeyCow \\ W + KeyCow \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} 83.60 \\ 85.64 \\ 86.98 \\ **87.23** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} 76.59 \\ 82.40 \\ 87.35 \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} 79.94 \\ 82.40 \\ 80.70 \\ 80.70 \\ 87.27 \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} 95.64 \\ 96.30 \\ 96.02 \\ \end{tabular} } \\ \cline{2-4}  & W + Key +Cow \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of Sougou Corpus. be ignored. Therefore, macro evaluation can reflect the overall classification results more objective for imbalance corpus. The background knowledge based single-streams: key-stream, cow-stream and keycow-stream, perform well in Reuter-21578 R8, as shown in Table 3. However, the results are not stable in other three corpus: the macro values have a slight rise or fall compared with word-stream, as shown in Tables 4-6. This is mainly caused by two aspects. On one hand, the contribution of background knowledge based feature extraction in single-stream has a great relationship of the quality of background knowledge and the training data distribution. On the other hand, the information extracted from background knowledge based stream are not as comprehensive as the word-stream. In background knowledge based stream, the inputs are keywords, co-occurred words or the both extracted from original text, while all words are taken as input in word-stream.