# Mining the Frequent Patterns of Named Entities for Long Document Classification
## 4 Experiments
### Experiments Results  
#### 4.3.1 Main Results  
The experiment results are presented in Table 2. Firstly, it shows that the SVM and the two well fine-tuned models, XGBoost and LGB, outperformed the other traditional machine learning methods. Regarding the best ACC and marco-F1 they achieved, SVM achieved 91.29% and 90.38% on CCF-BDCI 2020, and 85.41% and 70.82% on THUNews, respectively. The XGBoost achieved 62.17% and 52.26% on CCKS 2021-Level2, respectively. Additionally, the LGB achieved 90.42% and 63.12%, respectively. In addition, the comparison of the experimental results on CCF-BDCI 2020 and THUNews shows that the performances of the traditional machine learning-based methods declined when the types of text in the dataset became more numerous.  
Secondly, the BERT worked better than other deep learning-based methods on CCKS 2021-Level1. Regarding ACC and marco-F1, it achieved 92.70% and 66.96%, respectively. However, its performance on CCKS 2021-Level2, CCF-BDCI 2020, and THUNews was extremely poor. The reason for the poor performance of BERT on CCKS 2021-Level2 was probably that the number of samples was not enough, from three types to eight types, and the reason for the poor performance on the other two datasets was probably that the text lengths in CCF-BDCI 2020 and THUNews mostly exceeded the maximum sequence length, so some useful information placed beyond the maximum length would have been discarded by truncating, causing BERT to be incapable of capturing the features of the full text. About the hierarchical approach, the HAN, it had good results on the THUNews dataset. However, it is obvious that HAN underperformed on the first two smaller datasets. Furthermore, when dealing with extremely long documents, HAN still has to truncate them, which causes semantic loss.  
Thirdly, the experimental results also show that deep learning methods usually require large datasets to guarantee good performance. Their performances are superior to those of machine learning methods when the scale of the dataset is large enough.  
Finally, the MIPELD outperformed the baseline models in marco-F1 on both levels of CCKS 2021. For the accuracy rate on CCKS 2021-Level1, the BERT and the LGB achieved better scores; however, MIPELD obtained acceptable results, only lower than BERT's by around 3% and LGB around 1%. Furthermore, MIPELD achieved the best results on both CCKS 2021-Level2 and CCF-BDCI 2020. Although several deep learning-based models outperformed MIPELD on the THUNews dataset, MIPELD still achieved comparable results and outperformed all the traditional machine learning-based methods. Meanwhile, in comparison with the results of Doc2 Vec and Word2 Vec, the results indicate that the features mined by MIPELD are more significant. In particular, MIPELD is more advantageous on small scale datasets, which is more valuable for practical applications, because in real scenarios we often have to deal with small scale datasets. Moreover, compared with hierarchical models, MIPELD can provide more interpretability. The frequent itemsets of key features mined by MIPELD improve the confidence of its classification results.