# PNER: Applying the Pipeline Method to Resolve Nested Issues in Named Entity Recognition
## 3 Datasets
### GermEval 2014  
The GermEval 2014 dataset stands out as a comprehensive German named entity recognition resource, notable for its inclusion of nested entities. This dataset, meticulously compiled, derives its content from German news corpora and Wikipedia, ensuring a rich and varied linguistic representation. The dataset's annotation rigorously adheres to the labeling scheme developed by [PERSON] et al. [12], employing the \"BIO\" (beginning, inside, outside) tagging method for precision and clarity in entity identification.  
Mirroring the structure commonly found in domain-specific datasets, the GermEval 2014 dataset is methodically segmented into training, validation, and test sets. This segmentation facilitates a systematic approach to model training and evaluation, ensuring that the models developed are robust and effective across different data splits.  
A distinct feature of the GermEval 2014 dataset is its composition of predominantly everyday language corpora, as opposed to the specialized language found in medical datasets. This characteristic necessitated an in-depth statistical analysis of the main entities and sub-entities present within the dataset, with the findings detailed below.  
Table 3 provides the statistics of the GermEval 2014 dataset. It includes the counts of each category label as main entities and sub-entities. The items that appear in the table are described in detail in Table 4. It highlights that the dataset encompasses a total of 39,558 entities. An analysis of the distribution of these entities across various categories reveals an imbalance. However, an interesting observation emerges when the \"deriv\" (derivatives) and \"part\" (parts) categories are amalgamated with broader categories. When examining the dataset in terms of sub-entities, a significant pattern is observed. Entities categorized as \"LOC\" (locations) represent approximately 70.7% of the total entities across the training, validation, and test sets. This prevalence suggests a notable tendency within the German language corpus dataset: entities that act as nested entities are highly likely to fall under the \"LOC\" category. Such insights not only enhance our understanding of the dataset's structure but also inform strategies for effective entity recognition and extraction in the German language context.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Dev**} & \multicolumn{2}{c}{**Test**} & \multirow{2}{*}{**Overall**} \\ \cline{2-2} \cline{4-7}  & **Main** & **Sub** & **Main** & **Sub** & **Main** & **Sub** & \\ \hline PER & 7679 & 173 & 711 & 18 & 1639 & 47 & 10,267 \\ PERderiv & 62 & 5 & 2 & 0 & 11 & 1 & 81 \\ PERpart & 184 & 12 & 18 & 3 & 44 & 3 & 264 \\ LOC & 8281 & 907 & 763 & 78 & 1706 & 166 & 11,901 \\ LOCderiv & 2808 & 76 & 235 & 6 & 561 & 26 & 3712 \\ LOCpart & 513 & 21 & 52 & 1 & 109 & 2 & 698 \\ ORG & 5255 & 41 & 496 & 3 & 1150 & 8 & 6953 \\ ORGderiv & 41 & 0 & 3 & 0 & 8 & 1 & 53 \\ ORGpart & 805 & 2 & 91 & 1 & 172 & 0 & 1071 \\ OTH & 3024 & 20 & 269 & 0 & 697 & 4 & 4014 \\ OTHderiv & 236 & 2 & 16 & 0 & 39 & 0 & 293 \\ OTHpart & 190 & 0 & 18 & 0 & 42 & 1 & 251 \\ Overall & 29,078 & 1259 & 2674 & 110 & 6178 & 259 & 39,558 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics on the number of various category labels in the GermEval 2014 dataset.