# Background Knowledge Based Multi-Stream Neural Network for Text Classification
## 4 Experiments and Discussion
### Results and Discussion  
Results of Reuter-21578 R8 are shown as Table 3. From the results of single-stream in the table, it can be seen that in the single-streams based on background knowledge: key-stream, cow-stream and key-cow-stream, the macro precision, recall and F1 score have been significantly improved while the accuracy is not much different compared with the basal word-stream. In the multi-stream network, the overall macro value increased significantly while the accuracy also improved whether in early-fusion or after-fusion strategy. The highest three results under different evaluation indicators are bolded in the table. Under comprehensive consideration, the best one is obtained in three-stream network with average pooling (_P_ = _95.28_, \(R\) = _94.75_, _F1_ = _95.02_), which is superior than baseline (word-stream, \(P\) = _84.71_, \(R\) = _85.37_, _F1_ = _85.04_). The improved classification results mean that the incorporation of background knowledge has enriched the text representation to a great extent. Therefore, we can infer that the background knowledge can not only make up for the insufficient information of training data, but also make up for the deep feature extraction of those data sparse categories. The effectiveness of background knowledge incorporation in multi-stream neural network also verified in Reuters-20578 R52 and two Chinese corpus. The results of Reuters-20578 R52 are shown in Table 4, the best one acquired in three-stream network with after fusion of soft voting: \(P\) = _81.96_, \(R\) = _71.06_, _F1_ = _76.12_ (while \(P\) = _64.09_, \(R\) = _63.95_, _F1_ = _64.02_ in baseline). The results of SougouC corpus are shown in Table 5, the best one is acquired in three-stream network with early fusion: \(P\) = _87.23_, \(R\) = _87.30_, _F1_ = _87.27_ (while \(P\) = _83.72_, \(R\) = _83.30_, _F1_ = _83.36_ in baseline). The results of Fudan corpus are shown in Table 6, and the best one is acquired in three-stream network with early fusion: \(P\) = _88.69_, \(R\) = _81.65_, _F1_ = _85.03_ (while \(P\) = _76.70_, \(R\) = _76.41_, _F1_ = _76.55_ in baseline). In this way, we can infer that the background knowledge with multi-stream network contributes a lot for text classification task. \begin{table}
\begin{tabular}{c c c} \hline
**Abbreviations** & **Note** \\ \hline \(W\) & word-stream, input is _words_. \\ _Key_ & key-stream, input is _key_. \\ _Cow_ & cow-stream, input is _cov_. \\ _KeyCow_ & keycow-stream, input is concatenate of _key_ and _cov_. \\ \hline \(W\) + _Key_ & Fusion of word-stream and key-stream \\ \(W\) + _Cow_ & Fusion of word-stream and cow-stream \\ \(W\) + _KeyCow_ & Fusion of word-stream and keycow-stream \\ \(W\) + _Key_ + _Cow_ & Fusion of word-stream, key-stream and cow-stream \\ \hline \end{tabular}
\end{table} 
Table 1: Explanatory note of Abbreviations in the experiments. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Reuter R8** & **Reuter R52** & **Fudan** & **Sougou** \\ \hline Max length of word\_stream (_n_) & 128 & 100 & 300 & 300 \\ Max length of key\_stream (_m_) & 45 & 27 & 130 & 20 \\ Max length of cow\_stream (_t_) & 70 & 56 & 222 & 92 \\ \hline \end{tabular}
\end{table} 
Table 2: Max length setting of inputs.