# Chinese Named Entity Recognition Model Based on Multi-Task Learning
## 4 Experiment and Analysis  
### Dataset  
The experiments were conducted on several publicly available datasets, including Weibo NER [24], MSRA [25], CCKS2019 medical, self-built agricultural pest, OntoNote4.0 Chinese, and AG001. Weibo NER is a Chinese named entity recognition dataset in the social media domain, consisting of geographic (GPE), person (PER), location (LOC), and organization (ORG) entity categories, further divided into specific entity (named entity, NE) and referential entity (nominal mention, NM) categories at a finer granularity, posing a challenge to the entity identification task. The MSRA dataset, published by Microsoft Asia Research Institute, contains three entity types: Person (PER), Organization (ORG), and Location (LOC). OntoNote4.0 Chinese data is a mixed English and Chinese language corpus containing four entities: Geography (Location), Organization (ORG), Geography (GPE), and Person (PER). CCKS2019 is a dataset designed for named entity recognition of Chinese electronic medical records, consisting of six types of entities, including diseases and diagnoses (DISEASES), examinations (EXAMINATIONS), tests (TEST), surgeries (TREATMENT), drugs (DRUG), and anatomical sites ( BODY). The experiments also included comparisons of multitask learning in English and Chinese, with experiments conducted on the English corpus CoNLL-2003 [26] and the MIT-Movie dataset. Table 1 presents the basic information of the above datasets. Self-Built Chinese Agricultural Dataset AG001  
This dataset is designed for the named entity identification task in agriculture to improve the accuracy and recall of named entity identification in agriculture, which is critical for subsequent tasks such as pest and disease warning and control recommendations. The diversity, specialization, and locality of entities related to agriculture make named entity identification in agriculture an important and challenging task. Existing datasets often have limited coverage and cannot meet the requirements of different domains and scenarios. To solve this problem, we extracted agriculture-related entities, such as crop names, pest names, and treatments, from pest intelligence in Changshan and Lin'an, and built a Chinese named entity recognition dataset. The dataset contains 13,000 texts, each of which is no more than 256 characters long. To ensure that each entity in the dataset contains the semantic meaning of the context, the text was pre-processed with word granularity only to reduce the input dimension of \begin{table}
\begin{tabular}{c c c c c} \hline
**Data Set** & **Number of Enities** & **Training Set** & **Validation Set** & **Test Set** \\ \hline Weibo NER 1 & 8 & 1.4 k & 0.3 k & 0.3 k \\ MSRA 1 & 3 & 46.4 k & - & 4.4 k \\ OntoNote4.0 1 & 4 & 11.0 k & 2.3 k & 2.3 k \\ CCKS2019 & 6 & 6.5 k & 1.4 k & 1.4 k \\ AG001 & 3 & 2.1 k & 0.4 k & 0.4 k \\ CoNLL-2003 2 & 4 & 15.0 k & 3.5 k & 3.7 k \\ MIT-Movie 2 & 12 & 7.8 k & - & 2.0 k \\ \hline \end{tabular} 1 Chinese dataset, 2 English dataset.  
\end{table} 
Table 1: Dataset information. the model, while retaining the semantic information of the text. After that, data cleaning is performed to remove the noise and irrelevant information in the text, which improves the generalization ability of the model and reduces the risk of overfitting. Finally, tagging with BIO is used on the dataset, where each entity is tagged with a B-entity category at the beginning, an I-entity category at the middle and end, and O for non-entities, to facilitate learning the boundaries and categories of named entities. The dataset includes three entity categories: crop name (FIS), pest name (DIS), and treatment (TES). We divided the dataset into training, validation, and test sets in the ratio of 7:1.5:1.5. Our dataset is sufficiently difficult and representative to effectively test the model for domain-specific named entity recognition and to demonstrate and evaluate the robustness and generality of the proposed model. Examples of the datasets are provided below.