# K-EPIC: Entity-PerceIved Context Representation in Korean Relation Extraction
## 5 Conclusions  
In this paper, we propose K-EPIC, four different methods of representing entities in Korean Relation Extraction. To consider a Korean linguistic property as well as entity representation, we employ most Korean pre-trained language models and achieve enhanced performance rather than mBERT. The experimental results demonstrate that, by using entity position tokens, the capability of understanding the entities of pre-trained language models significantly improved. We evaluate the methods in each language model and find that K-EPIC\({}_{S}\) shows the best performance in most language models. Finally, we present the experimental results on the comparison of different tokenizers indicating that morpheme-level tokenization impacts the performance effectively. In conclusion, our work may suggest  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Data** & \multicolumn{4}{c}{**BERT-Ko-RE Dataset**} & \multicolumn{4}{c}{**KLUE-RE Dataset**} \\ \hline
**Model** & **[PERSON] w/Wordpiece** & **[PERSON] w/Morpheme** & **[PERSON] w/Wordpiece** & **[PERSON] w/Morpheme** \\ \hline
**Metric (F1)** & **Micro** & **Weighted** & **Micro** & **Weighted** & **Micro** & **Weighted** \\ \hline nonK-EPIC & 32.26 & 33.89 & 35.43 & 34.26 & 14.68 & 14.14 & 21.62 & 21.94 \\ K-EPIC\({}_{V}\) & 58.32 & 61.86 & 68.88 & 73.84 & 31.67 & 30.46 & 56.00 & 57.10 \\ K-EPIC\({}_{S}\) & 60.45 & 64.43 & **72.13** & **77.14** & 32.97 & 31.97 & 57.79 & 58.38 \\ K-EPIC\({}_{E}\) & 57.02 & 59.60 & 68.48 & 73.27 & 33.82 & 32.38 & 57.46 & 58.26 \\ K-EPIC\({}_{SE}\) & **61.59** & **65.69** & 67.94 & 73.90 & **34.40** & **32.50** & **58.61** & **58.54** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results between different tokenizers on KorBERT. Underlined text denotes the highest score of the KorBERT with a morpheme-based tokenizer and bold text indicates the highest score among the methods of utilizing K-EPIC.  
Figure 4: (**a**) Performance on each language model on the BERT-Ko-RE dataset. The green and pink bars indicate the performance of K-EPIC\({}_{S}\) and K-EPIC\({}_{SE}\), respectively; (**b**) performance on each language model on the KLUE-RE dataset. The green and pink bars indicate the performance of K-EPIC\({}_{S}\) and K-EPIC\({}_{SE}\), respectively.  
that the performance of a model in Korean RE highly depends on the understanding level of the entities in sentences while preserving Korean linguistic properties.  
Conceptualization, Y.H.; methodology, Y.H.; software, S.S.; validation, S.S.; formal analysis, Y.H. and J.L.; investigation, M.S.; writing--original draft preparation/review and editing, Y.H., S.S., M.S. and J.L.; visualization, M.S. and J.L.; funding acquisition/project administration/supervision, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1A6A1A03045425). Additionally, it was also supported by the MSIT (Ministry of Science and ICT), Korea, under the ICT Creative Consilience program (IITP-2021-2020-0-01819) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).  
Not applicable.  
**Data Availability Statement**: Publicly available datasets were analyzed in this study. These data can be found here: \"[[https://github.com/machinereading/bert-ko-re](https://github.com/machinereading/bert-ko-re)]([https://github.com/machinereading/bert-ko-re](https://github.com/machinereading/bert-ko-re)) (accessed on 1 March 2021)\" and \"[[https://github.com/KLUE-benchmark/KLUE](https://github.com/KLUE-benchmark/KLUE)]([https://github.com/KLUE-benchmark/KLUE](https://github.com/KLUE-benchmark/KLUE)) (accessed on 1 March 2021)\".  
The authors declare no conflict of interest.