## 4 Evaluation
### Experimental Results  
First, we evaluated the effectiveness of the k-stacked feature fusion layer and the dual-stacked output layer; the results are summarized in Table 5. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline The dimension of character embedding & 50 \\ The dimension of POS embedding & 16 \\ The dimension of hidden node in the feature fusion layer & 128 \\ The dimension of hidden node in the output layer & 256 \\ The dimension of Coarse-grained NE embedding & 768 \\ The dimension of Fine-grained NE embedding & 512 \\ Batch size & 64 \\ Learning rate & 0.001 \\ Epoch & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model parameters. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Coarse-Grained NE Tag** & **Description** & **Percent** \\ \hline QT & Quantity & 15.9\% \\ DT & Date & 14.0\% \\ OG & Organization & 12.6\% \\ TR & Theroy & 8.7\% \\ CV & Civilization & 8.2\% \\ \hline \hline
**Fine-Grained NE Tag** & **Description** & **Percent** \\ \hline TR-Technology & The technology of Theory & 7.6\% \\ DT-Year & The Year of Date & 5.3\% \\ DT-Month & The month of Date & 4.8\% \\ PS-Name & The name of Person & 4.7\% \\ OG-Business & The business of Organization & 3.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Distribution of NE tags which mainly occurred in the corpus. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** \\ \hline
1-In+1-Out & 0.783 & 0.681 & 0.728 \\
3-In+1-Out & 0.831 & 0.730 & 0.777 \\
3-In(H)+1-Out & 0.844 & 0.752 & 0.795 \\ \hline
1-In+2-Out & 0.808 & 0.701 & 0.750 \\
3-In+2-Out & 0.849 & 0.760 & 0.801 \\
3-In(H)+2-Out & 0.865 & 0.769 & 0.814 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison depending on changes in the architecture. In Table 5, \"k-In\", \"k-In(H)\", \"1-Out\", and \"2-Out\" denote a k-stacked feature fusion layer in which a flat concatenation of all input embeddings (i.e., a character trigram embedding, a POS trigram embedding, and a dictionary embedding) is fed into the first layer, the proposed feature fusion layer in which different levels of input embeddings are hierarchically fed into the k-stacked LSTMs, a single output layer (i.e., only the upper output layer), and a dual-stacked output layer, respectively. The results in Table 5 show that the models with a dual-stacked output layer always outperformed the models with a single output layer. This reveals that the proposed dual-stacked output layer contributes to alleviate the problem of unbalanced training data. In addition, \"3-In(H)+2-Out\" delivered the best performance. This reveals that the hierarchical feature embeddings in a stacked feature fusion layer are able to effectively hand over different levels of linguistic features to an output layer. The second experiment was conducted to compare the performance of the proposed model with those of the previous fine-grained NER models; the results are summarized in Table 6. In this table, \"KoELECTRA-NER\" is an NER model in which the character-based ELECTRA model [20] in Korean is fine-tuned to a sequence-labeling task. ELECTRA is a pre-trained language model with SOTA performance in many downstream NLP tasks, such as span prediction, sequence labeling, and text classification. We carried out a Korean NER task by pre-training KoELECTRA by using 96M sentences with 2.6B tokens. Then, we fine-tuned KoELECTRA by using the training NE dataset. \"BI-LSTM-LAN\" [21] is an NER model that simultaneously performs morphological analysis and coarse-grained NER in Korean. This model achieved SOTA performance by outperforming Korean NER models that did not use large pre-trained language models, such as BERT [22], ALBERT [23], and ELECTRA. The results presented in Table 6 show that \"3-In(H)+2-Out\" outperformed \"KoELECTRA-NER\" in the fine-grained NER task.