# K-EPIC: Entity-PerceIved Context Representation in Korean Relation Extraction
## 4 Experiments
### Results and Analysis
#### 4.3.3 Comparison on Korean Tokenizers  
Due to the fact that the Korean language is significantly different from English, the impact of tokenizers should be examined as well. KorBERT, which provides two different  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Data**} & \multicolumn{10}{c}{**BERT-Ko-RE dataset**} \\ \hline
**Model** & **mBERT** & **HanBERT** & **KLUE-BERT** & **KoBERT** & **KorBERT** & **KoeLECTRA** & \\ \hline
**Metric (F1)** & **Micro** & **Weighted** & **Micro** & **Weighted** & **Micro** & **Weighted** & **Micro** & **Weighted** & **Micro** & **Weighted** & **Avg.\%p** \\ \hline non-K-EPIC & 39.39 & 39.41 & 39.55 & 39.09 & 39.39 & 37.95 & 39.45 & 38.32 & 35.43 & 34.26 & 39.55 & 38.27 & +0.00 \\ K-EPIC\({}_{V}\) & 70.40 & 74.91 & 69.15 & 74.97 & 71.76 & 76.24 & 63.06 & 67.75 & 68.88 & 73.84 & **70.24** & **75.41** & +35.97 \\ K-EPIC\({}_{S}\) & **73.56** & **78.88** & **73.72** & 78.50 & **77.53** & **81.43** & **73.34** & **78.07** & **72.13** & **77.14** & 68.61 & 75.21 & **+0.43.2** \\ K-EPIC\({}_{E}\) & 69.97 & 75.65 & 70.78 & 75.27 & 72.14 & 76.49 & 66.21 & 71.90 & 68.48 & 73.27 & 67.41 & 72.34 & +36.27 \\ K-EPIC\({}_{SE}\) & 70.29 & 75.80 & 73.07 & **78.62** & 74.86 & 80.11 & 69.53 & 75.18 & 67.94 & 73.90 & 68.23 & 73.07 & +38.23 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results on the BERT-Ko-RE dataset on six language models within the K-EPIC method and average %p is calculated by subtracting non-K-EPIC from the average of weighted-F1 score of all of the language models (the best performing part is additionally marked in bold).  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Data**} & \multicolumn{10}{c}{**KLUE-RE Dataset**} \\ \hline
**Model** & **mBERT** & **HanBERT** & **KLUE-BERT** & **KoBERT** & **KorBERT** & **KoeLECTRA** & \\ \hline
**Metric (F1)** & **Micro** & **Weighted** & **Micro** & **Weighted** & **Micro** & **Weighted** & **Micro** & **Weighted** & **Micro** & **Weighted** & **Avg.\%p** \\ \hline nonK-EPIC & 20.91 & 20.34 & 23.64 & 22.84 & 23.54 & 23.97 & 22.84 & 21.81 & 21.62 & 21.94 & 23.03 & 22.31 & +0.00 \\ K-EPIC\({}_{V}\) & 53.43 & 52.66 & 56.23 & 55.44 & 60.57 & 59.29 & 54.39 & 53.33 & 56.00 & 57.10 & 50.89 & 50.03 & +32.44 \\ K-EPIC\({}_{S}\) & 54.27 & 53.71 & **59.43** & **58.92** & **61.23** & **60.77** & **56.57** & **56.05** & 57.79 & 58.38 & 57.80 & 57.24 & **+35.31** \\ K-EPIC\({}_{E}\) & 55.18 & 53.70 & 57.82 & 58.06 & 60.75 & 59.69 & 54.75 & 54.77 & 57.46 & 58.26 & 58.02 & 57.82 & +34.85 \\ K-EPIC\({}_{SE}\) & **55.78** & **54.59** & 59.01 & 57.79 & 60.31 & 59.79 & 55.82 & 54.88 & **58.61** & **58.54** & **59.29** & **58.51** & +35.15 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental Results on the KLUE-RE dataset on six language models within the K-EPIC method and average %p is calculated by subtracting nonK-EPIC from the average of weighted-F1 score of all of the language models (the best performing part is additionally marked in bold).  
tokenizers on the same model architecture, we compare them under our K-EPIC method. One is the KorBERT model with word-level tokenizers and the other is that with the morphome-level tokenizers. We conduct the experiments on the both BERT-Ko-RE and KLUE-RE datasets with the K-EPIC method. As indicated in Table 6, the model with morpheme-level tokenizers achieves a significantly higher score than that with word-level tokenizers in both datasets. The results can be attributed to the linguistic property of the Korean language, which is an agglutinative language where words may contain different postpositions according to the role of the word [31]. Along with these results, K-EPIC\({}_{SE}\) shows the best performance in comparison to other K-EPIC methods and nonK-EPIC in most cases.