• The Korean Journal of Community Living Science
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• v.28 no.2
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• pp.217-228
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• 2017

This paper examined the effects of children's interests and attitudes toward mother's native culture and use of mother's native language on satisfaction of the mother-child relationship in multi-cultural families. Data from the 2012 National Survey of Multi-cultural Families demonstrate that for children aged between 9 and 12 years, their fluency and desire to speak well in the mother's native language as well as father's encouragement for using the mother's native language at home were positively associated with satisfaction of the mother-child relationship. For those aged between 13 and 18 years, mother's nationality (i.e. Southeast or South Asia) was negatively related with mother-child relationship satisfaction. Both mother's and children' communication skills, children's interests in mother's native culture, pride for mother being a foreigner, and desire to speak well in the mother's native language were positively associated with mother-child relationship satisfaction. Therefore, there is a need for foreign wives to be educated in Korean language and culture as well as opportunities for children to learn their mother's native culture and language.

• Journal of Distribution Science
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• v.16 no.11
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• pp.5-10
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• 2018

Purpose - The recent establishment of many varieties of English language in the globe has created many models of English such as world Englishes (WEs), English as a Lingua Franca (ELF), English as a family of languages, and English as an Intercultural Language (EIcL). Among the models, the present study highlights 'English as an intercultural language (EIcL)' in relation to distribution science business English teaching to elucidate what EIcL is and why it is critical and how it can be realized in the business English classrooms. Research design, data, and methodology - This study look into the EIcL paradigm that empowers all active users to view English as universal and at the same time enables them to develop critical skills to bridge intercultural gaps or to cross borders. Results - Rather than just focusing on an acquisition of standardized English(es), EIcL serves as a major contextual factor facilitating success in getting competence among the different English languages. Conclusions - EIcL is a promising and ultimately rewarding approach to the contemporary business English teaching arena. EIcL should be achieved through policies, textbooks or living abroad, and, above all, learners/teachers' active awareness and understanding' of the EIcL mainstreams.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2049-2068
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• 2021

AI combined with NLP techniques has promoted the use of Virtual Assistants and have made people rely on them for many diverse uses. Conversational Agents are the most promising technique that assists computer users through their operation. An important challenge in developing Conversational Agents globally is transferring the groundbreaking expertise obtained in English to other languages. AI is making it possible to transfer this learning. There is a dire need to develop systems that understand secular languages. One such difficult language is Hindi, which is the fourth most spoken language in the world. Semantic similarity is an important part of Natural Language Processing, which involves applications such as ontology learning and information extraction, for developing conversational agents. Most of the research is concentrated on English and other European languages. This paper presents a Corpus-based word semantic similarity measure for Hindi. An experiment involving the translation of the English benchmark dataset to Hindi is performed, investigating the incorporation of the corpus, with human and machine similarity ratings. A significant correlation to the human intuition and the algorithm ratings has been calculated for analyzing the accuracy of the proposed similarity measures. The method can be adapted in various applications of word semantic similarity or module for any other language.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.489-497
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• 2007

Named entity translation plays an important role in many applications, such as information retrieval and machine translation. In this paper, we focus on translating person names, the most common type of name entity in Korean-Chinese cross language information retrieval (KCIR). Unlike other languages, Chinese uses characters (ideographs), which makes person name translation difficult because one syllable may map to several Chinese characters. We propose an effective hybrid person name translation method to improve the performance of KCIR. First, we use Wikipedia as a translation tool based on the inter-language links between the Korean edition and the Chinese or English editions. Second, we adopt the Naver people search engine to find the query name's Chinese or English translation. Third, we extract Korean-English transliteration pairs from Google snippets, and then search for the English-Chinese transliteration in the database of Taiwan's Central News Agency or in Google. The performance of KCIR using our method is over five times better than that of a dictionary-based system. The mean average precision is 0.3490 and the average recall is 0.7534. The method can deal with Chinese, Japanese, Korean, as well as non-CJK person name translation from Korean to Chinese. Hence, it substantially improves the performance of KCIR.

• Korean Journal of Radiology
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• v.25 no.2
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• pp.126-133
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• 2024

Large language models (LLMs) have revolutionized the global landscape of technology beyond natural language processing. Owing to their extensive pre-training on vast datasets, contemporary LLMs can handle tasks ranging from general functionalities to domain-specific areas, such as radiology, without additional fine-tuning. General-purpose chatbots based on LLMs can optimize the efficiency of radiologists in terms of their professional work and research endeavors. Importantly, these LLMs are on a trajectory of rapid evolution, wherein challenges such as "hallucination," high training cost, and efficiency issues are addressed, along with the inclusion of multimodal inputs. In this review, we aim to offer conceptual knowledge and actionable guidance to radiologists interested in utilizing LLMs through a succinct overview of the topic and a summary of radiology-specific aspects, from the beginning to potential future directions.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.89-94
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• 2024

The main purpose of the study is to determine the main elements of the use of digital technologies for learning a foreign language in educational institutions. The era of digital technologies is a transition from the traditional format of working with information to a digital format. This is the era of the total domination of digital technologies. Digital technologies have gained an unprecedented rapid and general distribution. In recent years, all spheres of human life have already undergone the intervention of digital technologies. Therefore, it is precisely the educational industry that faces a difficult task - to move to a new level of education, where digital technologies will be actively used, allowing you to conveniently and quickly work in the information field for more effective learning and development. The study has limitations and they relate to the fact that the practical activities of the process of using digital technologies in the system of preparing the study of a foreign language were not taken into account.

• Language and Information
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• v.19 no.2
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• pp.1-18
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• 2015

This paper retraces the development of Database Semantics (DBS) from its beginnings in Montague grammar. It describes the changes over the course of four decades and explains why they were seen to be necessary. DBS was designed to answer the central theoretical question for building a talking robot: How does the mechanism of natural language communication work? For doing what is requested and reporting what is going on, a talking robot requires not only language but also non-language cognition. The contents of non-language cognition are re-used as the meanings of the language surfaces. Robot-externally, DBS handles the language-based transfer of content by using nothing but modality-dependent unanalyzed external surfaces such as sound shapes or dots on paper, produced in the speak mode and recognized n the hear mode. Robot-internally, DBS reconstructs cognition by integrating linguistic notions like functor-argument and coordination, philosophical notions like concept-, pointer-, and baptism-based reference, and notions of computer science like input-output, interface, data structure, algorithm, database schema, and functional flow.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.144-150
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• 2023

With progressive advancements in Man-made consciousness (computer based intelligence) and Profound Learning (DL), contributing altogether to Normal Language Handling (NLP), the precision and nature of Machine Interpretation (MT) has worked on complex. There is a discussion, but that its no time like the present the human interpretation became immaterial or excess. All things considered, human flaws are consistently dealt with by its own creations. With the utilization of brain networks in machine interpretation, its been as of late guaranteed that keen frameworks can now decipher at standard with human interpreters. In any case, simulated intelligence is as yet not without any trace of issues related with handling of a language, let be the intricacies and complexities common of interpretation. Then, at that point, comes the innate predispositions while planning smart frameworks. How we plan these frameworks relies upon what our identity is, subsequently setting in a one-sided perspective and social encounters. Given the variety of language designs and societies they address, their taking care of by keen machines, even with profound learning abilities, with human proficiency looks exceptionally far-fetched, at any rate, for the time being.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.22-30
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• 2019

For effective human-robot interaction, robots need to understand the current situation context well, but also the robots need to transfer its understanding to the human participant in efficient way. The most convenient way to deliver robot's understanding to the human participant is that the robot expresses its understanding using voice and natural language. Recently, the artificial intelligence for video understanding and natural language process has been developed very rapidly especially based on deep learning. Thus, this paper proposes robot vision to audio description method using deep learning. The applied deep learning model is a pipeline of two deep learning models for generating natural language sentence from robot vision and generating voice from the generated natural language sentence. Also, we conduct the real robot experiment to show the effectiveness of our method in human-robot interaction.

• ETRI Journal
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• v.46 no.1
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• pp.106-117
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• 2024

Large language models (LLMs) have revolutionized various applications in natural language processing and exhibited proficiency in generating programming code. We propose a framework for evaluating the code generation ability of LLMs and introduce a new metric, pass-ratio@n, which captures the granularity of accuracy according to the pass rate of test cases. The framework is intended to be fully automatic to handle the repetitive work involved in generating prompts, conducting inferences, and executing the generated codes. A preliminary evaluation focusing on the prompt detail, problem publication date, and difficulty level demonstrates the successful integration of our framework with the LeetCode coding platform and highlights the applicability of the pass-ratio@n metric.