## 1 Introduction
### Embedding Layer: BERT Model  
The BERT pre-trained model is a transfer learning method that involves training the model using a large amount of text data unrelated to the target task. Based on this pre-training, the model parameters are fine-tuned using a training set specific to the target domain, thereby accomplishing particular natural language processing tasks. In this paper, we use the ChineseBERT model, which is pre-trained on the Chinese Wikipedia corpus, and fine-tune the model parameters using general corpora from PKU and a self-constructed text dataset of the coral reef ecosystem to optimize its performance in the specific task. By leveraging bidirectional context information in pre-training, BERT better captures semantics and context, effectively addressing polysemy. BERT's use of the Masked Language Model (MLM) and Next Sentence Prediction (NSP) tasks during pre-training not only enhances the quality of word vectors but also strengthens the model's contextual understanding [19]. These innovations have led BERT to excel in various natural language processing tasks, underscoring its significance in the field.  
BERT's input design includes unique innovations, incorporating word vectors, segment vectors, and position vectors to capture comprehensive word information, as illustrated in Figure 2. In the task of named entity recognition for marine coral reef ecosystems, special tokens such as [CLS] and [SEP] provide clear sentence boundaries and entity separation cues. For instance, the [CLS] token aids in recognizing the beginning of a sentence, while [SEP] is used to differentiate between entities or sentences. These elements work together to enhance the model's understanding of text structure, which is crucial for recognizing proper nouns.  
Figure 1: BERT-BiGRU-Att-CRF Chinese Coral Reef Ecosystem Named Entity Recognition Model architecture.  
At its core, BERT is built on multiple layers of bidirectional Transformer encoders, which, through self-attention mechanisms and feed-forward neural networks, capture input data features. The introduction of self-attention allows the model to focus on words at different positions within the input sequence, effectively capturing contextual information. This is vital for accurately identifying specialized terms in coral reef ecosystems. By processing input vectors, BERT generates rich semantic representations for texts in the marine ecology domain, laying the groundwork for precise terminology recognition. The BERT network structure is depicted in Figure 3. During training, [PERSON] uses two unsupervised tasks, MLM and NSP [20], to enhance its understanding of language models. MLM enables BERT to learn and predict masked words, deepening its contextual understanding, particularly crucial in marine ecological texts rich in terminology. NSP, on the other hand, trains the model to recognize logical relationships between sentences. The combination of these training tasks provides [PERSON] with a profound understanding of language, making it particularly suitable for complex tasks like marine ecological named entity recognition.  
The flexibility and exceptional contextual understanding capabilities of the BERT model make it an ideal choice for specialized domain tasks, such as named entity recognition in marine coral reef ecosystems.