• Cross-Cultural Studies
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• v.46
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• pp.327-353
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• 2017

This study analyzed how an aspect of translation can be shown on the 'Kureru type(くれる類)' verbs in "Kokoro", which was a Japanese modern novel when it was reproduced by translators. It focused on 'the use in accordance with a subject of expression and the other person' and 'the object of movement'. 'The use in accordance with a subject of expression and the other person' could be summarized as follows: The 'Kureru type' verbs were not translated only in accordance with the use of vocabulary in a dictionary. 'Kudasaru' was used in many examples of letter writing when 'the giver' was younger and it was translated to a polite form in Korean. 'Kureru' had a characteristic when 'the giver' was older in Korean translation. The act of parents was translated to an honorific form if parents were 'givers' regardless of whether a listener was an internal character or an external character in parent-child relationships. The degree of politeness was different in English translation when the 'Kureru type' verbs were used for asking a favor request command. 'Please' was used more for 'Kudasaru' than 'Kureru'. An aspect of translation in accordance with 'the object of movement' could be summarized as follows: The 'Kureru type' verbs were used as main verbs. 'Kureru' and 'Kudasaru' were translated to 'Juda' 'Jusida' in Korean translation, but they were translated to various vocabulary words in accordance with the characteristic of 'the object of movement' and were translated to imply a specific act, the process of possession and the result of possession in English translation. The 'Kureru type' verbs were also used as auxiliary verbs. The translated vocabulary words for Korean translation and English translation were different in accordance with whether the movement of things other than the movement of act was included or not. Examples were translated predominantly to expressions of profit such as '-Jada' '-Dalla' '-Jusida' when there was a movement of act as well as specific things in Korean translation. Also, some examples were translated to expressions of profit when there was the movement of act with an abstract matter and there was only the act of the object of movement, but many examples were translated to the act of first verbs. Examples were translated predominantly to the act of first verbs when there was the movement which included specific things and abstract matters or there was only the movement of act in English translation. Expressions of asking a favor request such as 'Kureru' and 'Kudasaru' were translated to '-Dalla' '-Juseyo' in Korean translation, but they were translated to expressions which specify an act while focusing on the structure of sentences or the function of language, such as 'must', 'ask', 'wish', 'would', and 'would like to' 'please' in English translation.

• Korean Journal of Heritage: History & Science
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• v.37
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• pp.243-266
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• 2004

This paper is based on fieldwork conducted from July 6, 2003 to July 24 of 2003 among the Tungusgroups Hezhe, Daur, Oloqun, Owenke, and Mongolian in the areas of Heilongjiang and Inner Mongolia Provinces. Recognizing the need for more in-depth study among these groups, the present research shows that the Tungus people are archeologically, historically, and linguistically different from Korean Han ethnic group and challenges the link between Korean and Tungus groups since the Bronze Age. The comparison between the "House Spirit" belief of the Tungus people and Koreans reveals certain commonalities in the "Maru," "Kitchen," and "Samshin Spirit" practices. There are two possible reasons for such commonalities. Historically, the Korean Han ethnic group and the Tungus people were geographically intimate, and contact or transmission between the two groups occurred naturally. Also, immigration of refugees from the fallen Koguryo and Puyo to the Tungus region added another dimension of cultural contact. In contrast to the common features shared between the two groups, there also exists differences between the two groups House Spirit blief. The Korean Han group's "House Spirit" belief is based on the agricultural practices that separates the inside sacred and outside secular world of the houses, whereas the Tungus ethnic group's "House Spirit" belief is based on mobile herding life style with a less distinction between in and outside of house. Additionally, each Korean "House Spirit" has its own distinctive personality, and each spirit is placed and worshipped according to its function. In the Tungus group, all the "House Spirits" are located and worshipped in "malu," and some of the spirits are non-conventional house spirits. Moreover, Korean "House Spirits" form a kinship structure, placing Songju, the highest spirit, at the center. In the Tungus practice, such structure is not found. The tight cohesive family formation among the house spirits in the Korean "House Spirit" belief is also the most distinctive feature in its comparison with Chinese belief. In China, the highest spirit is Jiang Taigong or Qiwu, and the house spirits do not have kinship relations. Korean's Outhouse Spirit and Chowangshin are related to the Han Chinese's counterpart on certain levels? however, their basic structures are different. It is clear that the correlation of "Malu" "Chowangshin" and "Samshin" between Korea and Tungus indicate important role of Tungus cultural elements within Korea's "House Spirit" belief.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.