# Empirical Analysis of Parallel Corpora and In-Depth Analysis Using LIWC
## 3 Analyzing the AI Hub Corpus Using LIWC
### Korean-English Parallel Corpus  
**Corpus Description**. The Korean-English parallel corpus ([[https://aihub.or.kr/aidata/87](https://aihub.or.kr/aidata/87)]([https://aihub.or.kr/aidata/87](https://aihub.or.kr/aidata/87)), accessed on 25 May 2022) is a parallel corpus from AI Hub, which was released in 2019. The Corresponding corpus was built through the cooperation of Saltlux partners ([[http://saltlux.com](http://saltlux.com)]([http://saltlux.com](http://saltlux.com)), accessed on 25 May 2022), Flitto ([[https://www.flitto.com](https://www.flitto.com)]([https://www.flitto.com](https://www.flitto.com)), accessed on 25 May 2022), and Evertran ([[http://www.evertran.com](http://www.evertran.com)]([http://www.evertran.com](http://www.evertran.com)), accessed on 25 May 2022). The total amount of sentence pairs in the constructed corpus is 1.6M, which comprises 800K news articles, 100K website contents from the government, 100K instances of by-law data, 100K instances of Korean-based cultural contexts, 400K instances of colloquial-style data, and 100K instances of dialogic data. The ratios of each domain to the entire corpus are shown in Figure 1. It can be considered the most representative Korean-English parallel corpus, and many Korean-related studies on MT have been conducted based on that corpus [55]. For a more thorough data analysis, we conducted an in-depth investigation of this corpus with respect to various features, such as morphemes, syntax information, and the characteristics of the corpus. We analyzed such features using LIWC, and the results are shown in Table 2.  
**Corpus Analysis**. We conducted analysis in the aspect of morphology through the linguistic dimension and the grammar of linguistic-feature results obtained using LIWC. In the linguistic dimension category, it shows high frequency in 'prepositions (prep)' and 'articles (article)' of the total part-of-speech, at 15.1% and 10.2%, respectively. Additionally, the 'auxiliary verb (auxverb)' in the grammar category appears for 6.75%, and its prevalence \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Ko-Zh Parallel Corpus (Social Science)**} & \multicolumn{4}{c}{**Ko-Zh Parallel Corpus (Technology)**} \\ \hline
**Categories** & **Features** & **Train** & **Valid** & **Total** & **Train** & **Valid** & **Total** \\ \hline \multirow{10}{*}{punctuations} & AllPunc & 66.01 & 63.97 & 65.79 & 63.19 & 63.24 & 63.2 \\  & Period & 0.15 & 0.15 & 0.15 & 0.1 & 0.12 & 0.1 \\  & **Comma** & **42.3** & **41.26** & **42.19** & **38.9** & **37.37** & **38.72** \\  & Colon & 1.09 & 0.94 & 1.07 & 1.03 & 0.82 & 1.01 \\  & SemiC & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 \\  & QMark & 0.18 & 0.13 & 0.17 & 0.05 & 0.05 & 0.05 \\  & Exclam & 0.05 & 0.06 & 0.05 & 0.02 & 0.02 & 0.02 \\  & Dash & 0.52 & 0.52 & 0.52 & 1.14 & 1.15 & 1.14 \\  & Quote & 15.52 & 14.31 & 15.39 & 12.68 & 12.89 & 12.7 \\  & Apostro & 0.52 & 0.46 & 0.51 & 0.57 & 0.5 & 0.56 \\  & Parenth & 3.79 & 4.28 & 3.84 & 6.65 & 8.53 & 6.87 \\  & OtherP & 1.89 & 1.86 & 1.89 & 2.05 & 1.79 & 2.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 1: Data-domain statistics of the Korean–English Parallel Corpus. is higher than that of others, such as 'commonverb (verb)', which shows the highest frequency, at 11.36%. This result indicates the continuous prevalence of be verbs (am, are, is, was, and were), and the perfect tense of English characteristics, such as diverse tenses and conjugations, was reflected as data, rather than a base form of a verb. In personal pronoun analysis, which consists of {'1 st pers singular (i)', '1 st pers plural (we)', '2 nd person (you)', '3 rd pers singular (shehe)', '3 rd pers plural (they)', and 'impersonal pronouns (ipron)', the frequency of impersonal pronouns (ipron)', and the '1 st pers singular(i)' has the highest frequency as one of the personal pronouns. The '2 nd person' and '3 rd pers singular' pronouns also came next in order of prevalence.