# PNER: Applying the Pipeline Method to Resolve Nested Issues in Named Entity Recognition
## 3 Datasets
### CMeEE Dataset  
The CMeEE dataset, tailored specifically for the Chinese medical domain, is a rich and comprehensive resource for researchers and practitioners. This dataset, encompassing a training set, validation set, and test set, was meticulously sourced from the AliTianchi platform and is presented in a JSON format for ease of use. The training and validation sets are particularly robust, featuring not only sentences but also detailed annotations. These include entities, the start index and end index of each entity, and entity categories.  
The test set, while providing only sentences, plays a crucial role in evaluating the effectiveness of models trained on this dataset. Once a model is trained, its performance can be rigorously tested using this set, and the results can be submitted to the Ali platform for a comprehensive evaluation. This process ensures the robustness and applicability of models in real-world medical scenarios.  
In our thorough analysis, we delved into the intricate details of the main entities and nested entities (sub-entities) present in the dataset. The findings of this detailed statistical examination are depicted in the table below.  
Table 1 reveals insightful data about the CMeEE dataset. It includes the counts of each category label as main entities and sub-entities. The items in the table are described in Table 2. It shows that the dataset is composed of a total of 79,111 entities. A closer look at the distribution of these entities across different categories reveals a significant imbalance. When considering the entities as main categories, the categories \"bod\" (body parts) and \"dis\" (diseases) dominate in terms of occurrences, with the \"sym\" (symptons) category following closely. Interestingly, the scenario shifts when we analyze the entities as sub-entities. Here, the \"bod\" category overwhelmingly constitutes about 84.7% of the total entities in both the training and test sets, underscoring its predominant role in the dataset's structure. The \"ite\" category, although smaller in quantity, stands out due to its proportional significance. Despite its lower overall count, it ranks second in frequency of occurrence as a sub-entity, highlighting its importance in the context of nested entities. This pattern suggests a characteristic feature of the Chinese medical dataset: if an entity contains nested entities, there is a high probability that these nested entities belong to the \"bod\" category.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Dev**} & \multirow{2}{*}{**Overall**} \\ \cline{2-2} \cline{4-6}  & **Main** & **Sub** & **Main** & **Sub** & \\ \hline dis & 15,664 & 155 & 4880 & 47 & 20,746 \\ sym & 10,115 & 2 & 3388 & 0 & 13,505 \\ pro & 6291 & 40 & 2039 & 16 & 8386 \\ equ & 878 & 9 & 235 & 3 & 1125 \\ dru & 3900 & 29 & 1433 & 7 & 5369 \\ ite & 2265 & 301 & 813 & 104 & 3483 \\ bod & 14,609 & 3063 & 4857 & 1019 & 23,548 \\ dep & 348 & 0 & 108 & 1 & 457 \\ mic & 1889 & 19 & 580 & 4 & 2492 \\ Overall & 55,959 & 3618 & 18,333 & 1201 & 79,111 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics on the number of various category labels in the CMeEE dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{dis} & \multirow{2}{*}{Disease} & \multicolumn{2}{c}{Diseases or syndromes; poisoning or injury; organ or cell damage} \\ \hline sym & Symptom & Symptoms; signs \\ \hline pro & Medical procedure & Diagnostic procedure; treatment or prevention procedure \\ \hline equ & Medical equipment & Diagnostic equipment; treatment equipment \\ \hline dru & Drug & Drugs \\ \hline ite & Medical test item & Medical test items \\ \hline bod & Body & Bodily substances; body parts \\ \hline dep & Department & Departments \\ \hline mic & Microorganism & Microorganisms \\ \hline \hline \end{tabular}
\end{table}
Table 2: Details of labels written in the CMeEE dataset.