• Language and Information
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• v.8 no.1
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• pp.27-52
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• 2004

This paper attempts to define various notions involving focus and topic found in Korean and also employs phonetic measures to verify the plausibility of those notions that are theoretically argued for. This paper crucially relies on Prince (1981) for the notion of familiarity and its pragmatic significance, and adopts Rooth's (1985) notion of alternative set and utilizes it in the light of pragmatic interpretation. The basic idea of this paper is to decompose the notion alternative set into finer-grained components and to assign various levels of familiarity to those finer components, thereby helping define different kinds of focus and topic according to the properties of those sub-components.

• Journal of the Korean Society for Library and Information Science
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• v.52 no.4
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• pp.91-110
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• 2018

We proposed a video summarization algorithm based on an ERP (Event Related Potentials)-based topic relevance model, a MMR (Maximal Marginal Relevance), and discriminant analysis to generate a semantically meaningful video skim. We then conducted implicit and explicit evaluations to evaluate our proposed ERP/MMR-based method. The results showed that in the implicit and explicit evaluations, the average scores of the ERP / MMR methods were statistically higher than the average score of the SBD (Shot Boundary Detection) method used as a competitive baseline, respectively. However, there was no statistically significant difference between the average score of ERP/MMR (${\lambda}=0.6$) method and that of ERP/MMR (${\lambda}=1.0$) method in both assessments.

• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.227-235
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• 2005

Document filtering is a task of deciding whether a document has relevance to a specified topic. As Internet and Web becomes wide-spread and the number of documents delivered by e-mail explosively grows the importance of text filtering increases as well. In this paper, we treat document filtering problem as binary document classification problem and we proposed the News Filtering system based on the Bayesian Classifier. For we perform filtering, we make an experiment to find out how many training documents, and how accurate relevance checks are needed.

• Health Policy and Management
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• v.11 no.4
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• pp.129-151
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• 2001

This study was conducted to describe trends of health services research (HSR) in Korea since 1968 and analyse the relevance of HSR to changes in health policy. Research methods are as follows: firstly, HSR articles were selected from 4 HSR related journals implicitly. Secondly, classification system of HSR was developed and then applied to previously selected papers in order to describe research trends. Finally, the frequency rankings of articles in research areas were compared with rankings in order of the importance of research area rated by experts. As a resesult, HSR articles have increased with time and three main research areas are health programme, health care financing, and health care organization/management. And many articles have been related to the efficiency and quality of health care since 1990. It seems HSR articles had little relevance to changes in health policy and policy environment. Especially, the recently disputed policy topic, namely the separation of prescription from disposing, has not received little attention since 1990. These findings suggest there is an urgent need for the reflection on HSR direction in Korea.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.250-259
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• 2019

Big data processing technology and artificial intelligence (AI) are increasingly attracting attention. Natural language processing is an important research area of artificial intelligence. In this paper, we use Korean news articles to extract topic distributions in documents and word distribution vectors in topics through LDA-based Topic Modeling. Then, we use Word2vec to vector words, and generate a weight matrix to derive the relevance SCORE considering the semantic relationship between the words. We propose a way to recommend documents in order of high score.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.754-759
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• 2007

The information recommendation system offers selected documents according to information needs of dynamic users. User's needs are expressed as profiles consisting of one or more words and may be changed into some specifics through relevance feedback made by users during the recommendation process. In previous research, users have entered relevance information by taking part in explicit relevance feedbacks and learned user profiles using the positive relevance feedbacks. In this paper, we learn user profiles using not only positive relevance feedback but negative relevance feedback and reinforcement learning. To compare the proposed with previous method, we performed experiments to evaluate recommendation performance of the same topic. As a result, the former shows the improved performance than the latter does.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.170-185
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• 2023

The advent of big data has brought about the need for analytics. Natural language processing (NLP), a field of big data, has received a lot of attention. Topic modeling among NLP is widely applied to identify key topics in various academic journals. The Korean Society of Industrial and Systems Engineering (KSIE) has published academic journals since 1978. To enhance its status, it is imperative to recognize the diversity of research domains. We have already discovered eight major research topics for papers published by KSIE from 1978 to 1999. As a follow-up study, we aim to identify major topics of research papers published in KSIE from 2000 to 2022. We performed topic modeling on 1,742 research papers during this period by using LDA and BERTopic which has recently attracted attention. BERTopic outperformed LDA by providing a set of coherent topic keywords that can effectively distinguish 36 topics found out this study. In terms of visualization techniques, pyLDAvis presented better two-dimensional scatter plots for the intertopic distance map than BERTopic. However, BERTopic provided much more diverse visualization methods to explore the relevance of 36 topics. BERTopic was also able to classify hot and cold topics by presenting 'topic over time' graphs that can identify topic trends over time.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.10a
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• pp.13-15
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• 2013

The purpose of this research is to provide the logistics research with a focus on the methods and theory of the research. This paper finds to explain the patterns of logistics research in domestic through the investigation of different theories and research methods employed. A analysis of methods in selected topics allows published research of the logistics to be categorized. Research classified into integrated table by each theory, research topic, methodology and triangulation. Study presented a variety of research paradigms in logistics and contributed to the current literature by providing an outlook of the logistics research.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.739-754
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• 2022

Usually, text data consists of many variables, and some of them are closely correlated. Such multi-collinearity often results in inefficient or inaccurate statistical analysis. For supervised learning, one can select features by examining the relationship between target variables and explanatory variables. On the other hand, for unsupervised learning, since target variables are absent, one cannot use such a feature selection procedure as in supervised learning. In this study, we propose a word selection procedure that employs topic models to find latent topics. We substitute topics for the target variables and select terms which show high relevance for each topic. Applying the procedure to real data, we found that the proposed word selection procedure can give clear topic interpretation by removing high-frequency words prevalent in various topics. In addition, we observed that, by applying the selected variables to the classifiers such as naïve Bayes classifiers and support vector machines, the proposed feature selection procedure gives results comparable to those obtained by using class label information.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.100-104
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• 2010

In the past decade, various search engines have been developed to retrieve web pages that web surfers want to find from world wide web. In search engines, one of the most important functions is to evaluate and rank web pages for a given web surfer query. The prior algorithms using hyperlink information like PageRank incur the problem of 'topic drift'. To solve the problem, relevance propagation models have been proposed. However, these models suffer from serious performance degradation, and thus cannot be employed in real search engines. In this paper, we propose a new ranking algorithm that alleviates the topic drift problem and also provides efficient performance. Through a variety of experiments, we verify the superiority of the proposed algorithm over prior ones.