## 3 Proposed Method
### Simple and Efficient Data Augmentation
#### 3.1.2 POS-Tagging Based Data Augmentation  
The first method proposed to construct wrong answer options is POS-tagging based data augmentation. As one of the features of TOEIC Part 5 mentioned earlier, semantic problems are characterized by a list of options with similar parts-of-speech, but with words that have different meanings [18,19]. Considering this characteristic, this method used POS-tagging to classify all word segments in the entire data based on parts-of-speech. Thereafter, it extracted three wrong answer options from the same word segment set of parts-of-speech as the correct answer option.  
After splitting the English sentence data into word segments and tagging each word segment through POS-tagging of NLTK, the word segments of the same parts-of-speech were classified in a manner such that they were included in the same set. If this process is conducted for the entire English sentence data, all the word segments existing in the data can be organized by parts-of-speech. Thus, word segment sets for a total of 39 parts-of-speech were generated by synthesizing all parts-of-speech existing in all sentences of the Korean-English parallel corpus. The parts-of-speech standard is the same as the parts-of-speech standard proposed by NLTK, and among them, only those included in this dataset were selected. Specific details can be found in the Table A1 of Appendix A.  
Among them, the set with a small number of word segments to be used as the wrong answer option may interfere with data diversity; thus, the sets with less than 30-word segments (TO, SYM, UH, WPs, LS, \", \") were excluded. In addition, preprocessing was conducted by excluding singular proper noun (NNP), plural proper noun (NNPS), and cardinals (CD), which do not focus on the meaning of words.  
When generating the data, the entire English sentence data was split into word segments with one of them being replaced with a blank to generate TOEIC data. At this time, by extracting three wrong answer options from the set of the same parts-of-speech as the actual correct answer option replaced with a blank, the data can be augmented similar to the semantic problems wherein options with different meanings but similar parts-of-speech are listed. Examples of the actual data generated through this methodology are presented in the POS-tagging Based Data column in Table 3.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multicolumn{2}{c}{**POS-Tagging Based Data**} & \multicolumn{2}{c}{**Lemmatizing Based Data**} \\ \hline
**Question** & **Options** & **Question** & **Options** \\ \hline \multirow{3}{*}{I think the \(\widehat{\text{\phantom{\text{\phantom{\text{\phantom{\text{\phantom{\text{\phantom{\text{\phantom{\text{\phantom  
As is evident from the POS-tagging Based Data column in Table 3, the data was augmented by reflecting the characteristics of the semantic problems, comprising options that had different meanings but similar parts-of-speech. When data was augmented in this manner, the model could better focus on the meaning corresponding to the blank in the sentence. Therefore, when solving the actual TOEIC problems, the model can better infer the correct answer to the intention of the problem through comparison to the options with a focus on meaning.