# Research on Climactic Chapter Recognition of a Chinese Long Novel Based on Plot Description
## 4 Experimental Design and Analysis
### Experimental Result and Analysis
SVM is suitable for dealing with a balanced classification problem, while the scales of the viewpoint paragraph and non-viewpoint paragraph in the experimental corpus are obviously imbalanced. However, the SVM method still performs better than NB due to two reasons: firstly, the structural risk minimization principle is used in this model, which can effectively avoid the occurrence of the \"overfitting\" problem, and therefore, this model has good generalization ability. Secondly, this model is very robust to the anomalies, which can effectively avoid the impact of anomalies on the recognition results. 3. BiLSTM is a typical deep learning model with strong feature-learning capability. This explains why it has a better recognition performance than traditional machine learning models, such as NB and SVM. The stacking of two layers enables this model to get rid of the limitation that it can only predict the output of the next moment based on the timing information of the previous moments. It can better combine the contextual information for feature extraction, and it is suitable for dealing with the novel text. However, the performance of this model is slightly worse than that of CCRPCM because it is committed to extracting semantic features of the novel text, but ignoring specific features for the climactic chapter recognition. 4. Roberta-large is a pre-trained language model based on the transformer structure. This model has better comprehension of the novel text by taking contextual information into account and applying the self-attention to capture the long-distance dependencies. Roberta-large performs better than BiLSTM on the experimental corpus of \"The Legend of the Condor Heroes\", but not quite as well on \"The Condor Heroes Return\" or \"Heaven Sword and Dragon Sabre\". The possible reasons include the large number of parameters, the lack of flexibility in structure, and the inflexible network. The purpose of the ablation experiment is to verify the effectiveness of a component in the proposed model. The ablation experimental results are shown in Table 5. Based on the experimental corpus of \"The Legend of the Condor Heroes\", the \(F1\) values of CCRPCM are 16.99%, 26.33%, 28.30%, and 6.46% higher than those of CCRPCM-PCM, CCRPCM-PDM, CCRPCM-BiGRU, and CCRPCM-MultiAtt, respectively. Based on the experimental corpus of \"The Condor Heroes Return\", the \(F1\) values of CCRPCM are 15.96%, 30.10%, 34.41%, and 7.07% higher, accordingly. Based on the experimental corpus of \"Heaven Sword and Dragon Sabre\", the \(F1\) values of CCRPCM are 4.37%, 14.51%, 25.87%, and 2.75% higher. This experiment verifies that CCRPCM-PCM has the best overall performance, followed by CCRPCM-MultiAtt and CCRPCM-PDM, CCRPCM-BiGRU with the worst performance. Reasons to explain this results could be as follows:  
1. BiGRU integrates the GRU model with different directions, fewer parameters, a faster training speed, and higher processing efficiency than BiLSTM. BiGRU is good at capturing the contextual semantic information of the novel text and can effectively inhibit the problems of \"gradient disappearance\" or \"gradient explosion\". That is why CCRPCM-PCM still has basically satisfying recognition performance under the default plot description matrix. 2. It can be seen from the mechanism of the BiGRU model that it tends to weaken the long-distance information or features, which is not conducive to climactic chapter recognition. The multi-head attention differentiates various features, and it concerns the important features for climactic chapter recognition, which contributes to improving the recognition performance. Therefore, the \(F1\) value of CCRPCM-MultiAtt is generally lower than that of CCRPCM. 3. The climactic chapter tends to change dramatically compared with its previous and subsequent chapters, which is represented by the chapter plot difference matrix. A plot description matrix is composed of a chapter plot matrix and a chapter plot difference matrix.