# Automatic Word Spacing of Korean Using Syllable and Morpheme
## 4 Experiment
### Evaluation and Result  
Table 5 shows the spacing correction performance of the proposed model. According to experimental results, morpheme-level, syllable-level, and both syllable and morpheme-level were used in order of good performance. When both the syllable and morpheme were used, the advantages of syllable and morpheme were combined to achieve better performance.  
Table 6 shows the spacing correction performance of the proposed model. According to experimental results, morpheme-level, syllable-level, and both syllable and morpheme-level were used in order of good performance. When both the syllable and morpheme were used, the advantages of syllable and morpheme were combined to achieve better performance.  
Table 6 shows the performance of the previous study of spacing correction and the performance of the proposed model. Previous studies [1; 8; 9; 13] used the Sejong corpus as test data to measure performance. In this study, the Sejong corpus was also used as test data. Reference [1] trained a spacing correction model using word frequency dictionary and syllable frequency dictionary-based word information and a morpheme analyzer, resulting in an f1 score of 93.2%. Reference [13] used a combination of unigram, bigram, trigram, and a noun dictionary. As a result of training a model combining gated recurrent unit (GRU) and CRF, an f1 score performance of 92.32% was achieved. Reference [9] constructed an encoder and a decoder using an LSTM-based sequence to sequence model. The attention mechanism technique was applied to the decoder, and the model was trained using sentence data limited to a maximum of 10 words. As a result, 2019 achieved an f1 score of 93.99%. Reference [8] created a feature vector through a Bi-LSTM encoder and constructed a model using a linear chain CRF. As a result of performance evaluation, an f1 score of 94.26% was achieved. In this study, a model was constructed with an architecture combining multiple filter 1D-CNN and Bi-LSTM, and a spacing correction model was trained by inputting two types of syllable-level and morpheme-level sequences. As a result, the performance of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Tag**} & \multicolumn{2}{c}{**Word**} \\ \cline{2-5}  & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline
2015 [1] & 98.06 & 92.27 & 94.15 & 93.20 \\
2016 [13] & 98.32 & 92.68 & 91.96 & 92.32 \\
2018 [9] & - & 93.72 & 94.27 & 93.99 \\
2019 [8] & 98.53 & 95.06 & 93.46 & 94.26 \\ Proposed model & 99.13 & 96.54 & 95.59 & **96.06** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with previous spacing studies. Marked in bold and underline is the best performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Input**} & \multicolumn{2}{c}{**Tag**} & \multicolumn{2}{c}{**Word**} \\ \cline{2-5}  & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline Morpheme & 98.29 & 93.00 & 92.96 & 92.98 \\ Syllable & 98.70 & 95.11 & 93.75 & 94.43 \\ Syllable and morpheme & 99.13 & 96.54 & 95.59 & **96.06** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance result of spacing correction. Marked in bold and underline is the best performance.  
proposed spacing correction model was 96.06%, which was about 1.8% higher than the previous study.