• Journal of Agricultural Extension & Community Development
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• v.1 no.1
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• pp.67-74
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• 1994

The objectives of this study were to review the present situations of computer use, computer education and training, and attitude to computer of agricultural extension agents. The research subjects were 279 agricultural extension agents in 15 City and Gun Rural Extension Offices Which were sampled by random sampling method among 182 City and Gun Rural Extension Offices. The major findings of the study were as follows; 1. Only 28% of the agricultural extension agents had personal computer in their home. 2. Agricultural extension agents mainly used word processor program, but hardly used data base, spreadsheet, and computer language. 3. About 40% of the respondents had not chance to be participated in computer education/training program yet. 4. Generally, rural change agents agreed that computer is very valuable for their job and that they should learn high computer technology. 5. Concludly, various contents of computer education/training program should be prepared for rural change agents and they should take full advantage of computer facilities.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.1014-1017
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• 2020

Many natural language processing (NLP) models utilize pre-trained word embeddings to leverage latent information. One of the most successful word embedding model is the Skip-gram (SG). In this paper, we propose a Skipgram drop (SG-Drop) model, which is a variation of the SG model. The SG-Drop model is designed to reduce training time efficiently. Furthermore, the SG-Drop allows controlling training time with its hyperparameter. It could train word embedding faster than reducing training epochs while better preserving the quality.

• Journal of Computing Science and Engineering
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• v.2 no.2
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• pp.162-179
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• 2008

We have developed a tool for annotation of electronic health record (EHR) data. Currently we are in the process of manually annotating a corpus of Norwegian general practitioners' EHRs with mainly linguistic information. The purpose of this project is to attain a linguistically annotated corpus of patient histories from general practice. This corpus will be put to future use in medical language processing and information extraction applications. The paper outlines some of our practical experiences from developing such a corpus and, in particular, the effects of semi-automated annotation. We have also done some preliminary experiments with part-of-speech tagging based on our corpus. The results indicated that relevant training data from the clinical domain gives better results for the tagging task in this domain than training the tagger on a corpus form a more general domain. We are planning to expand the corpus annotations with medical information at a later stage.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.313-326
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• 2020

Deep Learning is the most important key to the development of Artificial Intelligence (AI). There are several distinguishable architectures of neural networks such as MLP, CNN, and RNN. Among them, we try to understand one of the main architectures called Recurrent Neural Network (RNN) that differs from other networks in handling sequential data, including time series and texts. As one of the main tasks recently in Natural Language Processing (NLP), we consider Neural Machine Translation (NMT) using RNNs. We also summarize fundamental structures of the recurrent networks, and some topics of representing natural words to reasonable numeric vectors. We organize topics to understand estimation procedures from representing input source sequences to predict target translated sequences. In addition, we apply multiple translation models with Gated Recurrent Unites (GRUs) in Keras on English-Korean sentences that contain about 26,000 pairwise sequences in total from two different corpora, colloquialism and news. We verified some crucial factors that influence the quality of training. We found that loss decreases with more recurrent dimensions and using bidirectional RNN in the encoder when dealing with short sequences. We also computed BLEU scores which are the main measures of the translation performance, and compared them with the score from Google Translate using the same test sentences. We sum up some difficulties when training a proper translation model as well as dealing with Korean language. The use of Keras in Python for overall tasks from processing raw texts to evaluating the translation model also allows us to include some useful functions and vocabulary libraries as well.

• Journal of Home Health Care Nursing
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• v.23 no.1
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• pp.5-15
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• 2016

Purpose: This study was conducted to evaluate health service delivery and attitudes, toward multi-cultural clients amongst community health practitioners (CHPs). Methods: A survey was conducted among 242 CHPs from December 10-22, 2015. The collected data were analyzed using chi-square test, t-test, and ANOVA using SPSS 18.0. Results: General awareness of multi-culturalism varied significantly by CHPs age and language ability. Additionally, utilization of services in accordance with the location of community health centers (CHCs) was significantly higher in rural areas than urban areas CHCs in post-partum maternal & neonate care giver service (in maternal child health), management of health educational programs and management of physical exercise (in implementing healthy life style) and networking resources in & outside of CHCs (in management of chronic disease). Conclusion: CHPs deliver health-care services to multi-cultural clients, but have not received sufficient training or education to serve these clients effectively. CHPs who received multi-cultural and foreign language training had more positive experiences with multi-cultural clients. This supports the needs for developing educational programs to enhance multi-cultural understanding amongst CHPs.

• Annual Conference on Human and Language Technology
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• 2006.10e
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• pp.32-39
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• 2006

본 논문은 한국어정보처리 과정에서 구문 관계를 의미 관계로 사상하는 의미역 결정 문제에 대해 다루고 있다. 한국어의 경우 대량의 학습 말뭉치를 구하기 힘들며, 이를 구축하기 위해서는 많은 시간과 노력이 필요한 문제점이 있다. 따라서 본 논문에서는 학습 말뭉치를 직접 태깅하지 않고 격틀사전을 이용하여 자동으로 학습 말뭉치를 구축하고 간단한 확률모델을 적용하여 점진적으로 모델을 학습하는 수정된 self-training 알고리즘을 사용하였다. 실험 결과, 4개의 부사격 조사에 대해 평균적으로 81.81%의 정확률을 보였으며, 수정된 self-training 방법은 기존의 방법에 비해 성능 및 실행시간에서 개선된 결과를 보였다.

• International Journal of Advanced Culture Technology
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• v.9 no.2
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• pp.86-90
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• 2021

Sentiment refers to a person's thoughts, opinions, and feelings toward an object. Sentiment analysis is a process of collecting opinions on a specific target and classifying them according to their emotions, and applies to opinion mining that analyzes product reviews and reviews on the web. Companies and users can grasp the opinions of public opinion and come up with a way to do so. Recently, natural language processing models using the Transformer structure have appeared, and Google's BERT is a representative example. Afterwards, various models came out by remodeling the BERT. Among them, the Facebook AI team unveiled the XLM-R (XLM-RoBERTa), an upgraded XLM model. XLM-R solved the data limitation and the curse of multilinguality by training XLM with 2TB or more refined CC (CommonCrawl), not Wikipedia data. This model showed that the multilingual model has similar performance to the single language model when it is trained by adjusting the size of the model and the data required for training. Therefore, in this paper, we study the improvement of Korean sentiment analysis performed using a pre-trained XLM-R model that solved curse of multilinguality and improved performance.

• Journal of English Language & Literature
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• v.58 no.4
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• pp.679-700
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• 2012

This study aims to examine Mark Twain's criticism of American capitalistic ideals in the late nineteenth century. During this second industrial revolution, industry showed rapid growth and capitalism established an order, while America suffered under the monopolization of capitalistic conglomerates. This resulted in the widening gap between the rich and the poor and the dehumanization caused by rapid industrialization. In A Connecticut Yankee in King Arthur's Court, Hank Morgan, the protagonist--who represents nineteenth-century America's industrialism, individualism, and capitalism--is sent back in time to the sixth century of Arthurian England. Hank attempts to introduce nineteenth-century technologies and machines to build a capitalistic system in the middle ages. However, Hank's efforts lead to disaster in which the country and civilization he worked to build is completely destroyed. Although Twain does not deny capitalistic ideals, he criticizes the "governmentality" that operates Hank's reform system to the extreme. Hank values efficiency and utilizes human beings as capital. Hank's economic reason not only transforms the Round-Table knights into speculators but also transforms their religious acts and abstract ideals into moneymaking businesses. The destructive ending anticipates the World Wars and the Great Depression in the first half of twentieth century and even serves to predict the dangers that follow.

• Phonetics and Speech Sciences
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• v.15 no.4
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• pp.17-26
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• 2023

This study aimed to reveal how Vietnamese adult learners at three different proficiency levels perceive Korean word-initial obstruents and whether errors can be corrected through perceptual training. To this end, 105 Vietnamese beginner, intermediate, and advanced learners were given perceptual training on Korean word-initial. The training materials were created by actively utilizing Korean minimal pairs as natural stimuli recorded by native speakers. Learners in the experimental group performed five 20-40 minute self-directed perceptual training sessions over a period of approximately two weeks, while learners in the control group only participated in the pretest and posttest. The results showed a significant improvement in the perception of sounds that were difficult to distinguish before training, and both beginners and advanced learners benefited from the training. This study confirmed that large-scale perceptual training can play an important role in helping Vietnamese learners learn the appropriate acoustic cues to distinguish different sounds in Korean.

• The Korean Journal of Elementary Counseling
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• v.7 no.1
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• pp.205-226
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• 2008

The purpose of the study you have seen is to verify the effectiveness of existing quantitative research and to put the Empathy Training Program to practical use for participating children. From looking into this, the changes in empathic understanding that came to light in relationships between teacher and children and children and children are sure to have that effect. For this work, I established the following subject of inquiry: What kind of changing processes can be seen in the empathic understanding of participating children in the Empathy Training Program? To resolve the above line of inquiry, six female sixth grade elementary school students were chosen and they progressed through twelve sessions of the Empathy Training Program. The children were given a sentence completion exam, recognition work, neat writing exam and a school adaptation exam both before and after participation in the program, making data for analysis. To analyze, first, participants had one or two meetings of forty to fifty minutes each. Progress through the program's curriculum was recorded and through the repeating and copying method, to be sure participating children's empathic understanding was revealed, empathic language and behavior was routinely chosen. Next, according the above criteria I looked into visible changes of the participating children's empathic expressions, classifying and analyzing changes in empathic understanding and six instances of common changes in the emphatic understanding of the participants relationships were analyzed and put together. Next I will summarize the findings we have seen in this research: First, if we look into changes in common empathic understanding from the beginning, using the criteria of empathic language, each individual showed understanding at the beginning and passed and progressed through stages of care, insight and emotional expressions. Second, when we looked at the criteria of empathic behavior from the beginning to the end, one's line of vision and ability to concentrate one's attention was connected. Next, the act of nodding one's head looked like a brief nod at first but at the end, it was not just a simple nod but rather they could feel deep empathy. The condition and substance of the facial expression was seen to match and at the very end the child was expressive and stretched out arms to hold and pat the other person and the act of holding hands could also be seen. Among lots of empathic behavior the final stage was shown by half of the children. Third, from the first stage to the last stage there were many cases revealed. The more the children went the more complete their empathic language became. Their vocabulary increased and became more diverse with empathic actions. Also, when comparing actions and expressions from the beginning with the end, visible expressions became more natural and sincere at the end. The result of the research we have seen is that through receiving experience of empathic understanding, participating children showed a sense of self-confidence and they looked to make peaceful expressions while not being aggressive or defensive about problems. In addition, from understanding empathic expressions, participating children's relationships felt closer. This outcome within this group in this case will be applied and the formation of empathic understanding can be used by the children internally to solve their own problems, acquire close relationships with their teachers and others. It will also contribute to smooth classroom management.