• Journal of Intelligence and Information Systems
• /
• v.21 no.1
• /
• pp.1-13
• /
• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Journal of Distribution Science
• /
• v.13 no.2
• /
• pp.105-112
• /
• 2015

Purpose - There is a recent social trend that is focused on the revitalization of business-founding. Business-founding now has an important impact on the progress of the national economy because of youth unemployment and an increase in baby-boom generation retirees. However, the support and infrastructure required for business-founding of the disabled are very insufficient. Since most supporting policies are on youth or middle-aged business-founding, business-founding by the disabled and the socially weak is losing competitiveness. Accordingly, this study diagnosed the issues by analyzing the current status of business-founding by the disabled and suggested a fostering direction for the advance of business-founding by the disabled. An idea for the founding of various business items is required for the competitiveness of business-founding by the disabled and the establishment of a growth-model based on marketing is required so that business-founding by the disabled would advance toward commercialization with growth potential. Research design, data, and methodology - Regarding the study method, the existing study literature on the status and issues in business-founding was mainly explored. In addition, the existing literature on the status and issues in business-founding by the disabled was also studied. The support on business-founding by the disabled by policy enforced by the 'Welfare Service Agency for the Disabled'and the support of related agencies including financial support on the commercialization of business-founding by the disabled were also examined. Results - Existing studies on business-founding by the disabled are very insufficient. It is very difficult to study a viable business-founding by the disabled fostering policy without thorough learning on the difficulties of business-founding by the disabled. Therefore, this study suggested a direction for the resolution of various issues such as market, funds, item, operational matters, and service by analyzing the difficulties in business-founding by the disabled until now. Particularly, this study suggested that building a commercialization model from the aspect of marketing strategy and the effort to change the growth aspect of the disabled into competitiveness are essential. Conclusions - This study examined the aspect of developing an item-development process for the growth and founding of disabled-owned businesses and the requirement of a government support system by multiple policies. Since the number of studies on business-founding by the disabled is very small, it is expected that this study would become an important study in the field of business-founding by the disabled. The revitalization of business-founding by the disabled substantially contributes to the progress of the state of the economy and continuous interest is required from the viewpoint of equal advance in the society. Success models in business-founding by the disabled should be created continuously and active publicizing of them to the disabled business-founders by analyzing the success cases would also be required. In addition, it is believed that a market entry strategy by way of a win-win strategy and cooperative relation with big companies should be also developed in the future.

• Asian Pacific Journal of Cancer Prevention
• /
• v.16 no.2
• /
• pp.501-506
• /
• 2015

Background: To evaluate the effectiveness of the National Train-the-Trainers Program for Hospice and Palliative Care Experts (TTHPC) sponsored by the National Cancer Center of Korea between 2009 and 2012. This program was developed to improve the teaching skills of those in the field of hospice and palliative care (HPC). Materials and Methods: Training was offered in eight 1-day sessions between 2009 and 2012. The effect of the program was measured using Kirkpatrick's model of educational outcomes. First, levels 1 and 2 were evaluated immediately after the 1-day program (n=120). In 2012, the level-3 evaluation test was administered to trainers who offered at least one HPC training (n=78) as well as to their trainees (n=537). Results: The level-1 evaluation addressed participant reactions to and satisfaction with the program. Participants (n=120) were generally satisfied with the content, the method, and the overall course (mean range: 3.94-4.46 on a five-point Likert scale). The level-2 evaluation (learning) showed that participants gained knowledge and confidence related to teaching HPC (4.24 vs. 4.00). The level-3 evaluation (behavioral), which assessed trainers' application of teaching skills to HPC, showed that trainees rated the teaching methods of trainers (mean range: 4.03-4.08) more positively than did trainers (p<0.05). Female trainers were more likely than were male trainers to plan sessions in consideration of their trainees' characteristics (4.11 vs. 3.58; p<0.05), and nurse trainers were more likely than physician trainers to use a variety of instructional methods (4.05 vs. 3.36; p<0.05) Conclusions: We conducted systematic evaluations based on Kirkpatrick's model to assess the effectiveness of our train-the-trainers program. Our educational program was practical, effective, and followed by our HPC experts, who needed guidance to learn and improve their clinical teaching skills.

• Journal of Christian Education in Korea
• /
• v.68
• /
• pp.175-216
• /
• 2021

This essay aims to seek an alternative model of catechesis, as this form of education faces various challenges from the Korean Church especially during COVID-19 pandemic. For a long time, catechesis in the Korean Church narrowly focused on the act of producing Christians who would be loyal to the local church, rather than focusing on nurturing members loyal to Christ, an issue that has been problematized in recent publications on catechesis. Thus, the loss of social trust in the Korean Church and the decline of its public image exemplify how this type of catechesis as disciple-making for local church's benefit, mostly nurtures a vertical dimension of faith. The current teaching and learning method mostly employs a unilateral transfer of doctrine from the teacher to the learner and emphasizes the memorization of doctrine. This type of instruction renders the catechesis as the most lackluster and outdated form of Christian education. This essay aims to reconceptualize the traditional model of catechesis. This essay first critically evaluates current situations of catechesis and presents several alternative meanings on the concept of doctrine. Then it explores the theories of catechesis through different models posed by Christian educators such as John Westerhoff III and Richard Osmer. The final section is devoted to presenting an alternative form of catechesis that focuses on seeking holistic faith.

• Asia pacific journal of information systems
• /
• v.21 no.1
• /
• pp.103-122
• /
• 2011

Recently, numerous documents have been made available electronically. Internet search engines and digital libraries commonly return query results containing hundreds or even thousands of documents. In this situation, it is virtually impossible for users to examine complete documents to determine whether they might be useful for them. For this reason, some on-line documents are accompanied by a list of keywords specified by the authors in an effort to guide the users by facilitating the filtering process. In this way, a set of keywords is often considered a condensed version of the whole document and therefore plays an important role for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. Since many academic journals ask the authors to provide a list of five or six keywords on the first page of an article, keywords are most familiar in the context of journal articles. However, many other types of documents could not benefit from the use of keywords, including Web pages, email messages, news reports, magazine articles, and business papers. Although the potential benefit is large, the implementation itself is the obstacle; manually assigning keywords to all documents is a daunting task, or even impractical in that it is extremely tedious and time-consuming requiring a certain level of domain knowledge. Therefore, it is highly desirable to automate the keyword generation process. There are mainly two approaches to achieving this aim: keyword assignment approach and keyword extraction approach. Both approaches use machine learning methods and require, for training purposes, a set of documents with keywords already attached. In the former approach, there is a given set of vocabulary, and the aim is to match them to the texts. In other words, the keywords assignment approach seeks to select the words from a controlled vocabulary that best describes a document. Although this approach is domain dependent and is not easy to transfer and expand, it can generate implicit keywords that do not appear in a document. On the other hand, in the latter approach, the aim is to extract keywords with respect to their relevance in the text without prior vocabulary. In this approach, automatic keyword generation is treated as a classification task, and keywords are commonly extracted based on supervised learning techniques. Thus, keyword extraction algorithms classify candidate keywords in a document into positive or negative examples. Several systems such as Extractor and Kea were developed using keyword extraction approach. Most indicative words in a document are selected as keywords for that document and as a result, keywords extraction is limited to terms that appear in the document. Therefore, keywords extraction cannot generate implicit keywords that are not included in a document. According to the experiment results of Turney, about 64% to 90% of keywords assigned by the authors can be found in the full text of an article. Inversely, it also means that 10% to 36% of the keywords assigned by the authors do not appear in the article, which cannot be generated through keyword extraction algorithms. Our preliminary experiment result also shows that 37% of keywords assigned by the authors are not included in the full text. This is the reason why we have decided to adopt the keyword assignment approach. In this paper, we propose a new approach for automatic keyword assignment namely IVSM(Inverse Vector Space Model). The model is based on a vector space model. which is a conventional information retrieval model that represents documents and queries by vectors in a multidimensional space. IVSM generates an appropriate keyword set for a specific document by measuring the distance between the document and the keyword sets. The keyword assignment process of IVSM is as follows: (1) calculating the vector length of each keyword set based on each keyword weight; (2) preprocessing and parsing a target document that does not have keywords; (3) calculating the vector length of the target document based on the term frequency; (4) measuring the cosine similarity between each keyword set and the target document; and (5) generating keywords that have high similarity scores. Two keyword generation systems were implemented applying IVSM: IVSM system for Web-based community service and stand-alone IVSM system. Firstly, the IVSM system is implemented in a community service for sharing knowledge and opinions on current trends such as fashion, movies, social problems, and health information. The stand-alone IVSM system is dedicated to generating keywords for academic papers, and, indeed, it has been tested through a number of academic papers including those published by the Korean Association of Shipping and Logistics, the Korea Research Academy of Distribution Information, the Korea Logistics Society, the Korea Logistics Research Association, and the Korea Port Economic Association. We measured the performance of IVSM by the number of matches between the IVSM-generated keywords and the author-assigned keywords. According to our experiment, the precisions of IVSM applied to Web-based community service and academic journals were 0.75 and 0.71, respectively. The performance of both systems is much better than that of baseline systems that generate keywords based on simple probability. Also, IVSM shows comparable performance to Extractor that is a representative system of keyword extraction approach developed by Turney. As electronic documents increase, we expect that IVSM proposed in this paper can be applied to many electronic documents in Web-based community and digital library.

• The Journal of Society for e-Business Studies
• /
• v.19 no.4
• /
• pp.231-249
• /
• 2014

We define $21^{st}$ century as an amalgamation of globalization and localization, or Glocalization. Additionally, due to the increasing supply of smart phones and wide usage of social networking services, the ability to utilize such global and regional information has increased a coperation's competitiveness in its market, and even the business models have evolved from the conventional "production and distribution" to E-commerce, through which either a direct or a non-direct transaction is possible. My hypothesis is that the ability to adapt to this trend is possible through transfer of learning, and consequently, this will have an impact on company's performance. Thus, this thesis analyzes the mid- to the long-term impact of such ability and environmental factors on the performance and technology innovation of Korean companies in China. Ultimately, this study intends to engender a basic foundation for a corporation's management strategy in China. Finally this research focuses on those Korean companies in China only and on the proof of influential factors' impact on technological innovation and technological innovation's impact on those corporations' future performances. Section I is an abstract and section II, the case examines the uniqueness and current status of Korean companies in China identifies the concept and the definition of influential factors such as education-orientation, technological innovation, and performance, and then scrutinizes each factors through a closer look at their past researches. Section III explains the thesis model, the survey's method and target, the thesis, variable factors, the content, and the method of analysis. In section IV, the thesis is proved based on the outcome of the survey. The result in Section V highlights the high comprehension of technological innovation: both education-orientation and technological innovation prove to have a positive (+) correlation with the performance. The vision on education orientation proves to have a positive (+) influence on technological innovation. The vision on education-orientation and technological innovation prove to have a positive (+) influence individually on company's performance.

• Journal of Intelligence and Information Systems
• /
• v.21 no.2
• /
• pp.69-92
• /
• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.