## 2 Materials and Methods
### MGFGF-BiLSTM-EGP
### Evaluation Indicators

This study was evaluated using a confusion matrix, which categorized the model's prediction results into four groups: true positives (TPs), false positives (FPs), true negatives (TNs), and false negatives (FNs). The confusion matrix provides a detailed insight into the model's performance across these categories, allowing us to assess its predictive accuracy and identify both strengths and areas for improvement. Additionally, three key metrics were used to evaluate the model: precision, recall, and F1 score. Precision measures the proportion of correctly predicted positive samples out of all samples predicted as positive, with higher values indicating greater prediction accuracy. Recall assesses the model's ability to identify all actual positive samples, with higher values \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Optimizer & Adam \\ Warm up & 0.1 \\ LSTM units & 256 \\ Batch size & 32 \\ Dropout & 0.4 \\ Max len & 128 \\ Epoch & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental parameters. reflecting greater sensitivity. F1 score, the harmonic mean of precision and recall, offers a balanced assessment of the model's performance, where higher values indicate a more reliable and well-rounded model. \[P =\frac{TP}{TP+FP} \tag{23}\] \[R =\frac{TP}{TP+FN}\] (24) \[F1 =\frac{2\times P\times R}{P+R} \tag{25}\]

## 3 Results

We conducted experiments on three datasets, each divided into training, validation, and testing sets with a 6:2:2 ratio:

* CDNER (ours): The Chicken Disease Named Entity Recognition dataset comprising 5000 high-quality samples labeled by veterinary experts, containing five types of entities with nested structures. * CMEEE V2 [50]: A Chinese medicine entity recognition dataset with 20,000 annotated samples across nine categories including nested entities. The categories include diseases (dis), clinical symptoms (sym), drugs (dru), medical equipment (equ), medical procedures (pro), body (bod), medical examination items (ite), microbiology (mic), and departments (dep). * CLUENER [51]: A general domain NER dataset with 12,091 annotated samples across ten categories: addresses, books, companies, games, government, movies, names, organizations, positions, and scenes. For the CDNER dataset, we employed chicken disease word vectors trained on a 20 million characters corpus. For the CMEE V2 and CLUENER datasets, we utilized 200-dimensional Chinese word vectors from Tencent AI Lab [52]. ### Main Results Compared with Other Models

We evaluated the performance of several mainstream NER models on these datasets:

* Lattice LSTM [53]: Encodes input characters and all potential words in the matching dictionary. * FLAT [54]: A transformer-based model that utilizes unique position encoding to integrate the lattice structure, seamlessly introducing lexical information. * BERT-CRF: Combines a traditional CRF decoder with the BERT pre-trained model. * BERT-MRC [55]: Reframes the NER task as a reading comprehension problem. * SLRBC [56]: Integrates lexical boundary information using the SoftLexicon method with RoBERTa, BiLSTM, and CRF. As summarized in Table 3, the MFGFF-BiLSTM-EGP model consistently outperformed other models across all three datasets, achieving the highest F1 scores: 91.98% on CDNER, 73.32% on CMEE V2, and 82.54% on CLUENER. The superior performance of the MFGFF-BiLSTM-EGP model can be attributed to its integration of character, word, and sentence vectors, fused with BiLSTM and EPG, which enhanced the recognition accuracy by incorporating specialized vocabulary and contextual information. The SLRBC model also exhibited strong performance, particularly on the CDNER dataset, with an F1 score of 89.64%. This success was largely due to the SoftLexicon method, which enhanced vocabulary representation, combined with RoBERTa's robust contextual embeddings, BiLSTM's capacity to capture sequence data, and CRF's sequence tagging capabilities.