• Journal of Korean Society for Quality Management
• /
• v.44 no.1
• /
• pp.61-76
• /
• 2016

Purpose: In order to use a word as academic terminology, we must first take a look at the meaning of that word as it is commonly used and then consider whether or not the connotation of that word is suitable to be used as academic terminology. Presently, the word Pumjil(品質) is being used as academic terminology occupying an important position in the field of business administration in Korea and is usually translated into English as 'quality'. The same is true in Japan. However, as is the case with many Korean words, the meaning that the word implies has a tendency to change gradually over time. This tendency can account for the changes or additions to the meaning a word connotes. Methods: This dissertation aims to escape from such biased ideas and study the meaning of 'Pum-Jil品質' from the view of humanities and exegetics. Then the natural definition of the word as far as business administration is concerned can be considered. Results: 'Pum-Jil品質' has been used amid changes in modern times(historic texts in both Korea and China. In Korea particularly, the word was used in the royal court until comparatively modern times.), and now it is also widely used in the field of business administration. In this process of change, a notable point is that 'Pum-Jil品質', which was originally used to mean 'nature or character of a man', took on a new meaning, 'a certain quality of a thing or a good'. Conclusion: 'Pum-Jil品質' should require basic functional 'quality' of goods or services as a prerequisite. And the functional quality should meet consumers' needs, as the pledge (trust; 信賴) for quality is between suppliers and consumers. Without consumer's trust for goods, the relationship between suppliers and consumers cannot be maintained. So goods must exchange with trust, not expenses. In conclusion, we believe it is reasonable to understand 'Pum-Jil品質' based on the meaning of 'evidence or similar rating for pledge (trust)' from the view of humanities and exegetics. In conclusion, we believe it is reasonable to understand 'Pum-Jil品質' based on the meaning of 'evidence or similar rating for pledge (trust)' from the view of humanities and exegetics.

• Proceedings of the Korean Society for Language and Information Conference
• /
• 1994.02a
• /
• pp.133-142
• /
• 1994

• The Korean Journal of Franchise Management
• /
• v.12 no.2
• /
• pp.7-21
• /
• 2021

Purpose: One of the characteristics of service companies such as foodservice franchise is that it is easy to imitate, so many brands can imitate the menu that is popular with consumers. Therefore, foodservice franchise company should develop a brand that customers can identify from other brands in order differentiate it from its competitors. In order make the foodservice franchise company identifiable from other brands, it is possible through communication with customers. Therefore, this study proposes a new research model to analyze customer loyalty through online advertising and online word of mouth trust and immersion. Online was provided to customers through a mixture of advertisements and word of mouth, but previous studies have only considered online advertisements or online word of mouth. In addition, we want to verify the difference according to gender, which is an important variable in researching the online information processing behavior of customers. Research design, data, and methodology: The questionnaire of this study was surveyed on 20 years of age or older who have visited the restaurant franchise store within the last 3 months among the foodservice franchise companies operating SNS. During the survey period, 400 surveys were surveyed for a total of 20 days from April 1 to April 20, 2020. Result: The research results are as follows. First, in this study, the effect of online advertisement and online word of mouth on trust and immersion was studied. Second, this study verified the social influence theory in online advertising and online word of mouth. Third, the effect of online advertising and online word of mouth on loyalty according to gender was verified. Fourth, compared to existing advertisements, online advertisements are suitable for marketing by foodservice franchise companies because they can interact with consumers, modify advertisements immediately, execute extensive advertisements at low cost, segment the market, and measure advertisement effectiveness. The recent online expansion has been expanded to mobile-based, allowing foodservice franchisees to provide new communication services such as SMS (Short Message Service), multimedia messaging services, and location-based services. Fifth, a foodservice franchise company can increase brand awareness through online marketing or induce the use of offline stores. Sixth, franchisor can grow into a sustainable company only when they use resources efficiently. Conclusions: Trust is important in foodservice franchise information. This trust has a significant impact on customer commitment and loyalty.

• The Journal of Society for e-Business Studies
• /
• v.27 no.1
• /
• pp.63-79
• /
• 2022

Recently, various studies have been conducted on stock price prediction using machine learning and deep learning techniques. Among these studies, the latest studies have attempted to predict stock prices using limit order books, which contain buy and sell order information of stocks. However, most of the studies using limit order books consider only the trend of limit order books over the most recent period of a specified length, and few studies consider both the medium and short term trends of limit order books. Therefore, in this paper, we propose a deep learning-based prediction model that predicts stock price more accurately by considering both the medium and short term trends of limit order books. Moreover, the proposed model considers news headlines during the same period to reflect the qualitative status of the company in the stock price prediction. The proposed model extracts the features of changes in limit order books with CNNs and the features of news headlines using Word2vec, and combines these information to predict whether a particular company's stock will rise or fall the next day. We conducted experiments to predict the daily stock price fluctuations of five stocks (Amazon, Apple, Facebook, Google, Tesla) with the proposed model using the real NASDAQ limit order book data and news headline data, and the proposed model improved the accuracy by up to 17.66%p and the average by 14.47%p on average. In addition, we conducted a simulated investment with the proposed model and earned a minimum of $492.46 and a maximum of $2,840.93 depending on the stock for 21 business days.

• Journal of the Korean Society for information Management
• /
• v.31 no.1
• /
• pp.99-118
• /
• 2014

This paper carried out co-word analysis of noun and noun phrase using text-mining technique in order to grasp the research trends on domestic and international information science articles. It was conducted based on collected titles and articles of the papers published in the Journal of the Korean Society for Information Management (KOSIM) and Journal of American Society for Information Science and Technology (JASIST) from 1990 to 2013. By dividing whole period into five publication window, this paper was organized into the following processes: 1) analysis of high frequency co-word pair to examine the overall trends of both information science articles 2) analysis of each word appearing with high frequency keyword to grasp the detailed subject 3) focused network analysis of trend after 2010 when distinctively new keyword appeared. The result of the analysis shows that KOSIM has considerable portion of studies conducted regarding topics such as library, information service, information user and information organization. Whereas, JASIST has focused on studies regarding information retrieval, information user, web information, and bibliometrics.

• Journal of Information Management
• /
• v.41 no.2
• /
• pp.31-46
• /
• 2010

Now named-entity recognition(NER) as a part of information extraction has been used in the fields of information retrieval as well as question-answering systems. Unlike words, named-entities(NEs) are generated and changed steadily in documents on the Web, newspapers, and so on. The NE generation causes an unknown word problem and makes many application systems with NER difficult. In order to alleviate this problem, this paper proposes a new feature generation method for machine learning-based NER. In general features in machine learning-based NER are related with words, but entities in named-entity dictionaries are related to phrases. So the entities are not able to be directly used as features of the NER systems. This paper proposes an encoding scheme as a feature generation method which converts phrase entities into features of word units. Futhermore, due to this scheme, entities with semantic information in WordNet can be converted into features of the NER systems. Through our experiments we have shown that the performance is increased by about 6% of F1 score and the errors is reduced by about 38%.

• Journal of KIISE:Software and Applications
• /
• v.36 no.10
• /
• pp.861-873
• /
• 2009

There are many reasons that fail to get appropriate information in information retrieval. Allomorph is one of the reasons for search failure due to keyword mismatch. This research proposes a method to construct alternative word candidates automatically in order to minimize search failure due to keyword mismatch. Assuming that two words have similar meaning if they have similar co-occurrence words, the proposed method uses the concept of concentration, association word set, cosine similarity between association word sets and a filtering technique using confidence. Performance of the proposed method is evaluated using a manually extracted alternative list. Evaluation results show that the proposed method outperforms the context window overlapping in precision and recall.

• Journal of the Korean Society for information Management
• /
• v.30 no.4
• /
• pp.195-213
• /
• 2013

This study aims at establishing the identity of teaching Korean as a Foreign Language (KFL) domain by using journal profiling and co-word analysis in comparison with the relevant and adjacent domains. Firstly, by extracting and comparing topic terms, we calculate the similarity of academic journals of the three domains, KFL, teaching Korean as a Native Language (KNL), and Korean Linguistics (KL). The result shows that the journals of KFL form a distinct cluster from the others. The profiling analysis and co-word analysis are then conducted to visualize the relationship among all the three domains in order to uncover the characteristics of KFL. The findings show that KFL is more similar to KNL than to KL. Finally, the comparison of knowledge structures of these three domains based on the co-word analysis demonstrates the uniqueness of KFL as an independent domain in relation with the other relevant domains.

• The Transactions of the Korea Information Processing Society
• /
• v.3 no.5
• /
• pp.1273-1280
• /
• 1996

In order perform high accuracy recognition of text recognition systems, the recognized text must be processed through a post-processing stage using contextual information. We present a system that combines multiple knowledge sources to post-process the output of an optical character recognition(OCR) system. The multiple knowledge sources include characteristics of word, wrongly recognized types of Hangul characters, and Hangul word learning In this paper, the wrongly recognized characters which are made by OCR systems are collected and analyzed. We imput a Korean dictionary with approximately 15 0,000 words, and Korean language texts of Korean elementary/middle/high school. We found that only 10.7% words in Korean language texts of Korean elementary/middle /high school were used in a Korean dictionary. And we classified error types of Korean character recognition with OCR systems. For Hangul word learning, we utilized indexes of texts. With these multiple knowledge sources, we could predict a proper word in large candidate words.

• Journal of the Korea Society of Computer and Information
• /
• v.20 no.4
• /
• pp.17-23
• /
• 2015

In this paper, we propose a mobile app clustering method using word clusters. Considering the quick change of mobile app trends, the proposed method divides the mobile apps into some semantically similar mobile apps by applying a clustering algorithm to the mobile app set, rather than the predefined category system. In order to alleviate the data sparseness problem in the short mobile app description texts, the proposed method additionally utilizes the unigram, the bigram, the trigram, the cluster of each word. For the purpose of accurately clustering mobile apps, the proposed method manages to avoid exceedingly small or large mobile app clusters by using the word clusters. Experimental results show that the proposed method improves 22.18% from 57.48% to 79.66% on overall accuracy by using the word clusters.