# BERT-Based Transfer-Learning Approach for Nested Named-Entity Recognition Using Joint Labeling
## 7 Results and Discussion
### Comparison of the Results with Other Existing Approaches  
The best results of the pretrained BERT models for each of the three datasets were compared with the results of existing approaches. The comparison results for the GENIA dataset with the existing approaches are presented in Table 6.  
Similarly, a comparison was made in terms of performance for the GermEval 2014 dataset with the existing approaches. The comparison results for the GermEval 2014 dataset are presented in Table 7.  
A similar comparison was made for the JNLPBA dataset with the existing approaches. The comparison results for this dataset are presented in Table 8.  
A few important points observed from the above discussion of results are as follows:  
* On comparing the CRF, Bi-LSTM-CRF, and BERT-based language models, it was found that almost all the BERT-based models performed better than both the other models. The performance of the Bi-LSTM-CRF models was better than that of the CRF model, but not the fine-tuning-based, pretrained BERT-based models. * There was a huge impact of the language on the BERT-based model, which was clear from the results for the GermEval 2014 (German) nested NER dataset. Even the Bi-LSTM-CRF model performed poorly if the English GLOVE word vectors were used for the word embedding (due to which the German FastText word vectors were used only for the GermEval 2014 dataset). * The transfer-learning-based approach without any modifications or any external resources performed well on the GENIA, GermEval 2014, and JNLPBA datasets compared to many of the existing approaches. In Tables 6-8, comparisons were made with existing research work. There were a number of other research works in this area that achieved better results than the presented transfer-learning approach. Note that we are still far from the state-of-the-art results for the above three datasets. Our approach did not possess any kind of complexity in architecture or implementation. The same was not true for the other existing research works. In this study, we wanted to compare the performances of the pretrained, BERT-based transfer-learning approach without using any external resources such as embeddings, unsupervised training on the new dataset, etc. The study was conducted for a performance comparison between the pretrained BERT models based on domain, model size (base or large), and cased and uncased versions. * Domain-based pretrained models could perform significantly better than the other BERT models pretrained on different domains. For example, the BioBERT-based models performed better on the GENIA dataset, Google's multilingual BERT-based model performed better on the GermEval 2014 dataset, and the SciBERT-based model performed better on the JNLPBA dataset (followed by the BioBERT). * The model size of the pretrained BERT model can also put some impact on the results (in most cases). However, the result difference may not be very significant between the base and large models in all the cases. * In most cases (for the GENIA and GermEval 2014 datasets), the performance of the cased version of the model was better than that of the uncased version of the model. However, the uncased versions of the BERT language model performed better on the JNLPBA dataset. * Some of the common postprocessing methods from the existing research works have been carried to improve the prediction of best-performing models. However, the performance declined, rather than improving. So, postprocessing is not recommended for the named-entity-recognition problem. It is important to note that the existing approaches for the nested named-entity-recognition problem are complex. At the same time, the presented transfer-learning-based approach is much simpler than any of the other existing approaches and can be easily used for similar problems. In addition, it is important to note that the presented transfer-learning-based approach had no requirement for manual feature extraction or the word vectors, while these were needed for the conditional random field and Bi-LSTM-CRF models.