# Empirical Analysis of Parallel Corpora and In-Depth Analysis Using LIWC
## 3 Analyzing the AI Hub Corpus Using LIWC
### Korean-English Parallel Corpus (Social Science)  
**Corpus Description**. Similar to the technology-science specialized corpus, the social science specialized Korean-English parallel corpus ([[https://aihub.or.kr/aidata/30720](https://aihub.or.kr/aidata/30720)]([https://aihub.or.kr/aidata/30720](https://aihub.or.kr/aidata/30720)), accessed on 25 May 2022) was also published in 2021 through the cooperation of Twigfarm, Lexcode, Naver, TTA, and FNJ. The entire corpus comprises 1.5M sentence pairs, including 300K instances of economic data, 90K instances of cultural content, 100K instances of tourism content, 400K instances of education data, 500K instances of law data, and 110K instances of art domain content. The occupational ratio of each domain to the corresponding corpus is shown in Figure 4. The data was revised by the specialists of their domain and translation experts. In this study, we partially leveraged 537K sentence pairs (law (37.2%), economy (24.2%), education (24.1%), tourism (5.9%), culture (4.5%), art (4%), and medical(0.08%)) because all the data is yet to be released. **Corpus Analysis**. The data was also split into the training and validation datasets, and the results of running LIWC on the training, validation, and the entire dataset are shown in the Table 3. The difference between each linguistic feature of the training and validation datasets is mostly within 0.1%, and as an exception, some features, such as 'authentic' and 'emotional tone', have relatively sizable dissimilarities. This is as a result of the incidental blending of datasets with various domains, such as law, culture, and economy. First, the morphological analysis is homogeneous to the results presented in Section 3.2, but negates serving as not and never have been used the most among other corpora. This result is contrary to the outcomes in the technical science corpus discussed in Section 3.4, thereby suggesting that the frequency of plain and negative statements varies depending  
Figure 3: Data-domain statistics of the Korean–English Parallel Corpus (Technology). on the domain. In the case of pronouns, impersonal pronouns ('ipron') have been used four times more than personal pronouns ('ppron'), as shown in the results of Section 3.2, which is caused by the characteristics of the corpus in which the written sentences account for the description of most of the objects. Considering syntactic characteristics, the number of analytic explanations is relatively high, thereby indicating that this corpus logically describes domains, such as economics, law, and education. There is a higher ratio of 'Sixltr' compared to other corpora, thereby demonstrating the increased use of average long words. The low 'WPS' also suggests that short sentences have been used. We also find that future focus ('focusfuture') accounts for the smallest percentage in the time orientations category. The reason behind this output is that there exist present state-oriented explanations rather than future predictions in the social and cultural corpora. As a characteristic of the semantic perspective, the biological process category shows the lowest score compared to that of other corpora. Specifically, 'body (body)' figures are within 0.2% as a result of the nature of the social science domain, which is far from biology-related topics. Additionally, the cognitive process of human thinking is the highest compared to that of other corpora, with 1.5 to 1.8 times higher insight and cause. This confirms that 'insight (insight)' and 'cause (cause)' are attributes of written sentences in the social science domain in contrast to the technical science domain and specialized fields. The prevalence of 'posemo' is twice as high as that of 'negemo'. Subsequently, considering the phenomenon of gender bias, male-related pronouns are approximately twice as much as female-related pronouns, similar to other corpora.