• Journal of Korean Classical Literature and Education
• /
• no.16
• /
• pp.147-179
• /
• 2008

Classical literature texts have been transmitted and recreated as subjective meanings in a wide variety of genres. Authors recreate another world with their own imagination and classical literature texts. This study has been conducted to analysis 'OnDal's Dream' which is an adaptation from 'OnDalJeon' in order to figure out the method of inheritance and the subject and message of the adaptation. The process of inheritance and acculturation appears in the literary world. Some adaptations stick to the genre of the original text, other adaptations change to various genres. There is the process of inheritance and acculturation in the aspect of structure of the adaptation 'OnDal's Dream'. lntertextuality can be found between 'OnDalJeon' and 'OnDal's Dream' in terms of the stages such as 'confrontation' between the ego and the world and 'overcoming' hardships. However, the recreation text has acculturation of the structure that shows the potential desire for elevation of social status at the end part of the work so I could possibly show that the adaptation has limitation because it was far from the dream of common people and laborers at that time. There are different structures and recognition systems between 'OnDal Jeon' and 'OnDal's Dream' because the formal is an epic tale the other is lyric tale. An epic tale has some partial symbols in its story line, while an lyric tale is a symbol as a whole. There is an exhibition of deep emotion which is subjectivized and symbolized against the world in the adaptation 'OnDal's Dream'. And the inheritance of unreality, which is acculturation to the world of dream, from the original text can be found in the adaptation. First of all, study between the original text and the recreation text should be conducted in terms of intertextuality. Secondly, an instruction on the inheritance which is based on intertextuality between the original text and the recreation text should be conducted. Thirdly, an instruction about the structure of a genre and differences of recognition systems according to inheritance or conversion of a genre. It will be helpful for children to stimulate to take an interest in classical literature texts and traditional arts, to learn more recreation texts, and to develop the practical ability to recreate works. Based on above study, an instruction which shows a spiritual value of literature should be conducted.

• Journal of Popular Narrative
• /
• v.26 no.3
• /
• pp.291-323
• /
• 2020

In this paper, I review Pellegrino's Reader, The Philosophy of Medicine Reborn. Pellegrino has emphasized the humanities' reflection on the ethics of medicine. He insists that medical ethics should be re-established as modern society changes. This paper, based on Pellegrino's view, noted the problematic situation in literature and popular narrative texts. Indeed, I wanted to see what answer medical ethics could provide for us. Medical personnel had a philosophical dilemma or a conflict between reality and ethics. Pellegrino argues that medical personnel, above all, need to sympathize with the patient's pain and respond to their needs through interaction with them. This may seem like a very legitimate declaration. But a physician in literary texts and popular narrative texts is often exposed to this ethical dilemma. Through Lee Cheongjun's novel, we can reflect on how a medical personnel could lead a patient to a state of "goodness". And through medical dramas, we can grasp what ethical behaviors the public demands from a medical personnel. Now that the world is suffering from COVID-19, medical workers are in a great trouble, but at the same time, they are respected by the public and are also enhancing their value as ethical beings. Now that medical care has become an everyday narrative, medical ethics is becoming a prerequisite for living. This paper attempted to recognize the importance of medical ethics and to review the ethical issues embodied in medical narratives.

• Journal of Korean Elementary Science Education
• /
• v.41 no.2
• /
• pp.418-431
• /
• 2022

In this study, we analyzed the form, presentation, and interactivity of external representations presented in the matter units of elementary school science digital textbooks developed under the 2015 Revised National Curriculum. The analytic framework of the previous study was modified and supplemented. The matter units in the 3rd-6th grade science digital textbooks were analyzed by dividing them into "body texts" and "inquiries" area. The results revealed that visual-verbal and visual-nonverbal representations were presented the most. Conversely, audial-nonverbal representations were presented at a high frequency only in the body texts, and audial-verbal representations were presented at a low frequency in both the body texts and the inquiries. Regarding the presentation, when verbal and visual-nonverbal representations appeared together, visual-verbal and visual-nonverbal representations were primarily presented together. In some cases where visual-verbal, audial-verbal, and visual-nonverbal representations were presented together, information on visual-verbal and audial-verbal representations was presented redundantly. Audial-nonverbal representations unrelated to contents were presented along with other external representations, and the frequency was particularly high in the body texts. Regarding the contiguity, no visual-verbal and visual-nonverbal representations were presented on different pages, and no audial-verbal representations were presented asynchronously with visual-nonverbal representations. Regarding the interactivity, explanatory feedback and low-level manipulations were mainly presented. Based on the results, implications to improve digital textbooks are discussed from the perspective of multiple representation-based learning.

• Journal of Korea Society of Industrial Information Systems
• /
• v.29 no.3
• /
• pp.1-12
• /
• 2024

In this paper, we fine-tuned KoAlpaca, a large-scale Korean language model, and implemented a blog text generation system utilizing it. Blogs on social media platforms are widely used as a marketing tool for businesses. We constructed training data of positive reviews through emotion analysis and refinement of collected sponsored blog texts and applied QLoRA for the lightweight training of KoAlpaca. QLoRA is a fine-tuning approach that significantly reduces the memory usage required for training, with experiments in an environment with a parameter size of 12.8B showing up to a 58.8% decrease in memory usage compared to LoRA. To evaluate the generative performance of the fine-tuned model, texts generated from 100 inputs not included in the training data produced on average more than twice the number of words compared to the pre-trained model, with texts of positive sentiment also appearing more than twice as often. In a survey conducted for qualitative evaluation of generative performance, responses indicated that the fine-tuned model's generated outputs were more relevant to the given topics on average 77.5% of the time. This demonstrates that the positive review generation language model for sponsored content in this paper can enhance the efficiency of time management for content creation and ensure consistent marketing effects. However, to reduce the generation of content that deviates from the category of positive reviews due to elements of the pre-trained model, we plan to proceed with fine-tuning using the augmentation of training data.

• Journal of Intelligence and Information Systems
• /
• v.24 no.1
• /
• pp.1-23
• /
• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.

• Journal of the Korean Statistical Society
• /
• v.30 no.2
• /
• pp.219-230
• /
• 2001

How do you carry out data analysis\ulcorner There are few texts and little theory. One approach could be to use a pattern language, an idea which has been successful in field as diverse as town planning and software engineering. Patterns for data analysis are defined and discussed, illustrated with examples.

• Proceedings of the Korean Information Science Society Conference
• /
• 2010.11a
• /
• pp.173-174
• /
• 2010

• Proceedings of the Korea Information Processing Society Conference
• /
• 2001.04a
• /
• pp.293-296
• /
• 2001

병렬 코퍼스의 확보가 과거에 비해 용이하게 됨에 따라 기계번역, 다국어 정보 검색 등 언어처리시스템에 사용하기 위한 대역 사전 구축의 도구로서 정렬(Alignment) 기법에 대한 연구가 필요하다. 본 논문에서는 한국어-일본어 병렬 코퍼스를 이용한 정렬 기법에 관하여 제안한다.

• Journal of Korea Multimedia Society
• /
• v.17 no.9
• /
• pp.1125-1133
• /
• 2014

Recently, there are many researches have been studying for analyzing user interests and emotions based on users profiles and diverse information from Social Network Services (SNSs) due to their popularities. However, most of traditional researches are focusing on their researches based on single resource such as text, image, hash tag, and more, in order to obtain what user emotions are. Hence, this paper propose a method for obtaining user emotional information by analyzing texts and images both from Instagram which is one of the well-known image based SNSs. In order to extract emotional information from given images, we firstly apply GRAB-CUT algorithm to retrieve objects from given images. These retrieved objects will be regenerated by their representative colors, and compared with emotional vocabulary table for extracting which vocabularies are the most appropriate for the given images. Afterward, we will extract emotional vocabularies from text information in the comments for the given images, based on frequencies of adjective words. Finally, we will measure WUP similarities between adjective words and emotional words which extracted from the previous step. We believe that it is possible to obtain more precise user emotional information if we analyzed images and texts both time.

• Journal of Internet Computing and Services
• /
• v.13 no.2
• /
• pp.99-108
• /
• 2012

Many methods had been developed to improve the accuracy of extracting information from a vast amount of data. This paper combined a number of natural language processing methods such as NER (named entity recognition), sentence extraction, and part of speech tagging to carry out text analysis. The data source is comprised of texts obtained from the web using a domain-specific data extraction agent. A framework for the extraction of information from unstructured data was developed using the aforementioned natural language processing methods. We simulated the performance of our work in the extraction and analysis of texts for the detection of organizational structures. Simulation shows that our study outperformed other NER classifiers such as MUC and CoNLL on information extraction.