# Research on Named Entity Recognition Based on Gated Interaction Mechanisms
## 3 Results and Discussion
### Experimental Results and Analysis
Although the introduction of an additional deformation unit makes the training time of the model in this paper slightly longer than that of the BiGRU model, it is still significantly lower than the training time of the BiLSTM model. Moreover, the model in this paper also shows improved accuracy. In summary, the model proposed in this paper ensures the effectiveness of entity recognition while effectively reducing training costs, achieving a balance between time and performance. Combining the gated recurrent unit (GRU) with the gated interaction mechanism on the CLUENER2020 dataset, the experimental results are compared with the other two models as follows: On the CLUENER2020 dataset, the F1 value of the BERT-Mogrifier-BiGRU-CRF model has increased by 7.28% compared to the BERT-BiLSTM-CRF model structure, and by 1.4% compared to the BERT-BiGRU-CRF model structure. Compared to the BERT-BiLSTM-CRF model structure, there is a significant improvement in the recognition ability of all entities in the dataset. The recognition accuracy of entities with noticeable improvements reached 13.5%, 8.9%, and 8.4%. To further explore the superiority of the model proposed in this paper compared to other models, we collected training data with large improvement margins to find patterns. On the CLUENER2020 dataset, there is an imbalance in the number of entities in different categories. The BiLSTM-CRF model excessively focuses on entity categories with a larger number and neglects those with fewer entities. The gated interaction mechanism of the model proposed in this paper can adjust the weights of input representations, enabling the model to focus on entity categories with fewer entities as well. This allows the model to improve its recognition ability for entity categories with fewer entities without sacrificing its recognition ability for categories with more entities. Additionally, the BERT pre-trained model itself has learned rich semantic representations and possesses strong text representation capabilities through pre-training on large-scale text corpora. The model proposed in this paper achieved better results on the dataset compared to the BERT-BiLSTM-CRF model structure. Comparing the results of the model proposed in this paper with the BERT-BiGRU-CRF model on this dataset, the introduction of the gated interaction mechanism also slightly improved the accuracy of entity recognition, demonstrating the effectiveness of the gated interaction mechanism. To a certain extent, it has improved the recognition of Chinese fine-grained named entities. Furthermore, the model also showed good coping abilities for the polysemy phenomenon, where entities with the same name represent different types in different training corpora. In this study, we conducted a detailed statistical analysis of entities in sentences where some models made recognition errors on the CLUENER2020 dataset. The research results show that when faced with nested entity processing tasks, if the entity type appears in other training data, the model often only recognizes the entity type at the rear of the combined entity and cannot comprehensively and accurately determine the entire entity type. Additionally, for longer-nested entities existing in the data, the model's recognition effect is also unsatisfactory. After in-depth analysis, we believe that the reason for this phenomenon is that when annotating such entities in the training data, the annotation results are often not precise enough, leading to a decrease in the recognition accuracy of some entities.