# Enhanced Chinese Domain Named Entity Recognition:
Pretrained models, represented by BERT (bidirectional encoder representation from transformers) [19], have a significant advantage in extracting contextual information features from text due to training on large-scale datasets. This notably improves various metrics for natural language processing (NLP) tasks. In the field of NER, significant achievements have been made by methods based on fine-tuning pretrained models, like BERT [19; 20], BERT-CRF [21; 22], and BERT-BiLSTM-CRF [23]. Pretrained models can learn contextual features of text on the basis of training on large-sample data, enabling fine-tuning in situations with limited annotated data and subsequently learning contextual features for downstream tasks. The models mentioned above are based on sequence labeling to accomplish NER, which means performing a multi-classification task for each token. However, in the domain of Chinese paleontology popular science, as shown in Figure 1, the entity \"\(\texttt{\tiny{\#}}\texttt{\tiny{\#}}\texttt{\tiny{\#}}\texttt{\tiny{\#}} \texttt{\tiny{\#}}\texttt{\tiny{\#}}\texttt{\tiny{\#}}\texttt{\tiny{\#}}\texttt{ \tiny{\#}}\texttt{\tiny{\#}}\texttt{\tiny{\#}}\texttt{\tiny{\#}}\texttt{\tiny{ \#}}\texttt{\tiny{\#}}\texttt{\tiny{\#}}\texttt{\  
integration of lexicon-level features into Chinese NER has attracted widespread attention. Integrating lexicon-level features involves incorporating lexicons as external information, helping to determine the span and type of entities. At the same time, in our domain, we found that the distribution of domain-specific lexicon also affects the performance of NER. Currently, the utilization of distributional features of a domain-specific lexicon is still in its early stages. To address the above-mentioned issues, in this paper, we propose an enhanced NER method based on lexicon boundary and frequency weight features, named the BERT-Lexicon-PointerNetwork (BERT-LPN) model. The model is proposed for extracting Chinese paleontology popular science entities and it consists of an encoder based on BERT, a lexicon feature fusion layer and a fully connected classifier layer from the bottom up: the encoder is a Chinese character-level model based on BERT, and it maps Chinese characters to a low-dimensional, highly dense real-vector space to extract latent semantic information from Chinese entity elements; the lexicon feature fusion layer takes lexicon boundary and weight information from Chinese word segmentation as input to capture the lexicon-level features of the input; finally the fully connected classifier layer takes BERT-encoded embeddings along with lexicon-level features as input to generate start position and end position labels for entities. The experimental results indicate that our proposed model outperforms previous models, and our approach achieves state-of-the-art (SOTA) performance on the constructed dataset. The main contributions of this research are summarized as follows:  
* Based on the characteristics of texts in the domain of Chinese paleontology popular science, we propose a NER model enhanced with Chinese lexicon boundary and frequency weight features. This model is designed for identifying and extracting entities from unstructured textual data. * Based on the structural characteristics of our model, we propose a data augmentation method utilizing all entity types and sample entity types to alleviate the inconsistency between training and prediction tasks. To comprehensively evaluate the performance of our model, we established a new dataset, namely the PPOST dataset, specifically designed for NER in the Chinese paleontology popular science domain. The dataset primarily consists of data from authoritative institutions in the domain of paleontology popular science in China. Experimental results on both public datasets and the PPOST dataset validate the effectiveness of our approach. In addition, we also analyze the possibility of applying our method to other domains, providing a reference for enhancing NER performance in other domains.