• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.354-359
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• 2022

에세이 자동 평가는 주어진 에세이를 읽고 자동으로 평가하는 작업이다. 본 논문에서는 효과적인 에세이 자동 평가 모델을 위해 Argument Mining 작업을 사용하여 에세이의 논증 구조가 반영된 에세이 표현을 만들고, 에세이의 평가 항목별 표현을 학습하는 방법을 제안한다. 실험을 통해 제안하는 에세이 표현이 사전 학습 언어 모델로 얻은 표현보다 우수함을 입증했으며, 에세이 평가를 위해 평가 항목별로 다른 표현을 학습하는 것이 보다 효과적임을 보였다. 최종 제안 모델의 성능은 QWK 기준으로 0.543에서 0.627까지 향상되어 사람의 평가와 상당히 일치한다.

• The KIPS Transactions:PartB
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• v.19B no.2
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• pp.135-148
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• 2012

This paper proposes a model for recognizing sentential paraphrases through Predicate-Argument Tuple (PAT)-based approximate alignment between two texts. We cast the paraphrase recognition problem as a binary classification by defining and applying various alignment features which could effectively express the semantic relatedness between two sentences. Experiment confirmed the potential of our approach and error analysis revealed various paraphrase patterns not being solved by our system, which can help us devise methods for further performance improvement.

• Bulletin of the Korean Mathematical Society
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• v.56 no.3
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• pp.703-728
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• 2019

Considered herein is the orbital stability in the energy space $H^1({\mathbb{R}})$ of a decoupled sum of N peakons for a Camassa-Holm-type equation with quartic nonlinearity, which admits single peakon and multi-peakons. Based on our obtained result of the stability of a single peakon, then combining modulation argument with monotonicity of local energy $H^1$-norm, we get the stability of the sum of N peakons.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.985-995
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• 2015

Add-on selling can provide new chances to increase sellers' profits and meet customers' needs. Although prior studies have advocated add-on selling for its business value, there is an argument that add-on selling can cause customer repulsion. Therefore, we need to understand customer purchasing pattern related to add-on selling in order to promote it and to mitigate the customer repulsion. To that end, we applied data mining techniques to the 24,925 transactions of data from an online open market in Korea. We then conducted feature selection to investigate the most influential factors that can explain the characteristics of add-on selling transactions using a classification model. We also identified association rules among add-on selling and promotions. Finally, based on the findings in our experiments, we proposed add-on selling strategies for the target online market.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.177-197
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• 2001

This paper is aimed at proposing a data mining-driven analysis to manage the customer defection rate in the bank. After 1997 IMF crisis, Korean banks were suffering from hard-pressed restructuring. At the heart of such restructuring effects, there was the need to manage the customer more effectively than ever. So far, many banks in Korea used to a poor management of customers without any highly-skillful techniques. In line with this argument, we propose several data mining techniques to determine more effective technique far managing customer deflection. We applied three data mining techniques such as logit model, neural network, and C5.0. Experiment data were collected from personal deposit account data of a specific bank in Korea. After experiments, we found that C5.0 showed more robust performance compared to other two techniques. On the basis of those experiment results, we proposed customer defection management policy.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.234-238
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• 2023

미세 조정은 대부분의 연구에서 사전학습 모델을 위한 표준 기법으로 활용되고 있으나, 최근 초거대 모델의 등장과 환경 오염 등의 문제로 인해 더 효율적인 사전학습 모델 활용 방법이 요구되고 있다. 패턴 추출 학습은 사전학습 모델을 효율적으로 활용하기 위해 제안된 방법으로, 본 논문에서는 한국어 주장 탐지 및 입장 분류를 위해 패턴 추출 학습을 활용하는 모델을 구현하였다. 우리는 기존 미세 조정 방식 모델과의 비교 실험을 통해 본 논문에서 구현한 한국어 주장 탐지 및 입장 분류 모델이 사전학습 단계에서 학습한 모델의 내부 지식을 효과적으로 활용할 수 있음을 보였다.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.53-77
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• 2012

This study analyses the difference of contents and tones of arguments among three Korean major newspapers, the Kyunghyang Shinmoon, the HanKyoreh, and the Dong-A Ilbo. It is commonly accepted that newspapers in Korea explicitly deliver their own tone of arguments when they talk about some sensitive issues and topics. It could be controversial if readers of newspapers read the news without being aware of the type of tones of arguments because the contents and the tones of arguments can affect readers easily. Thus it is very desirable to have a new tool that can inform the readers of what tone of argument a newspaper has. This study presents the results of clustering and classification techniques as part of text mining analysis. We focus on six main subjects such as Culture, Politics, International, Editorial-opinion, Eco-business and National issues in newspapers, and attempt to identify differences and similarities among the newspapers. The basic unit of text mining analysis is a paragraph of news articles. This study uses a keyword-network analysis tool and visualizes relationships among keywords to make it easier to see the differences. Newspaper articles were gathered from KINDS, the Korean integrated news database system. KINDS preserves news articles of the Kyunghyang Shinmun, the HanKyoreh and the Dong-A Ilbo and these are open to the public. This study used these three Korean major newspapers from KINDS. About 3,030 articles from 2008 to 2012 were used. International, national issues and politics sections were gathered with some specific issues. The International section was collected with the keyword of 'Nuclear weapon of North Korea.' The National issues section was collected with the keyword of '4-major-river.' The Politics section was collected with the keyword of 'Tonghap-Jinbo Dang.' All of the articles from April 2012 to May 2012 of Eco-business, Culture and Editorial-opinion sections were also collected. All of the collected data were handled and edited into paragraphs. We got rid of stop-words using the Lucene Korean Module. We calculated keyword co-occurrence counts from the paired co-occurrence list of keywords in a paragraph. We made a co-occurrence matrix from the list. Once the co-occurrence matrix was built, we used the Cosine coefficient matrix as input for PFNet(Pathfinder Network). In order to analyze these three newspapers and find out the significant keywords in each paper, we analyzed the list of 10 highest frequency keywords and keyword-networks of 20 highest ranking frequency keywords to closely examine the relationships and show the detailed network map among keywords. We used NodeXL software to visualize the PFNet. After drawing all the networks, we compared the results with the classification results. Classification was firstly handled to identify how the tone of argument of a newspaper is different from others. Then, to analyze tones of arguments, all the paragraphs were divided into two types of tones, Positive tone and Negative tone. To identify and classify all of the tones of paragraphs and articles we had collected, supervised learning technique was used. The Na$\ddot{i}$ve Bayesian classifier algorithm provided in the MALLET package was used to classify all the paragraphs in articles. After classification, Precision, Recall and F-value were used to evaluate the results of classification. Based on the results of this study, three subjects such as Culture, Eco-business and Politics showed some differences in contents and tones of arguments among these three newspapers. In addition, for the National issues, tones of arguments on 4-major-rivers project were different from each other. It seems three newspapers have their own specific tone of argument in those sections. And keyword-networks showed different shapes with each other in the same period in the same section. It means that frequently appeared keywords in articles are different and their contents are comprised with different keywords. And the Positive-Negative classification showed the possibility of classifying newspapers' tones of arguments compared to others. These results indicate that the approach in this study is promising to be extended as a new tool to identify the different tones of arguments of newspapers.

• The Journal of Society for e-Business Studies
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• v.22 no.4
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• pp.71-101
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• 2017

Since late 2016, protectionism has been a major trend in world trade with the Great Britain exiting the European Union and the United States electing Donald Trump as the 45th president. Consequently, there has been a huge public outcry regarding the negative prospects of heavy industry firms in Korea, which are highly dependent upon international trade with Western countries including the United States. In light of such trend and concerns, we have tried to predict business performance of heavy industry firms in Korea with data regarding trade policy of the United States. United States International Trade Commission (USITC) levies countervailing duties and anti-dumping duties to firms that violate its fair-trade regulations. In this study, we have performed data analysis with past records of countervailing duties and anti-dumping duties. With results from clustering analysis, it could be concluded that trade policy trends of the Unites States significantly affects the business performance of heavy industry firms in Korea. Furthermore, we have attempted to quantify such effects by employing long short-term memory (LSTM), a popular neural networks model that is well-suited to deal with sequential data. Our major contribution is that we have succeeded in empirically validating the intuitive argument and also predicting the future trend with rigorous data mining techniques. With some improvements, our results are expected to be highly relevant to designing regulations regarding heavy industry in Korea.

• Journal of the Economic Geographical Society of Korea
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• v.17 no.1
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• pp.200-213
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• 2014

During the 1970s and 1980s, most of the industrial cities of the Western World went through serious de-industrialization process. While the economy of the cities declined, the cities were suffering from the lack of job opportunities, under-use of industrial infrastructure, and environmental degradation. Many of the cities attempted to tackle such problems, but very few of them have succeeded. However, the Ruhr area is showing some promising signs. This paper attempts to analyze regional revitalization policies of Dortmund, a central city of the Ruhr, and looks into the processes and outcomes of the policies. The author argues Dortmund has successfully created and set forth a new industrial path based on high-tech industries, such as information technology, micro-electromechanical systems(MEMS), logistics, and biomedical industries, away from its traditional industries, such as coal mining and steel making. Speaking from the perspective of path theory, he continues to argue that Dortmund has successfully 'locked-out' from the traditional industries, in contrast to the Grabher's argument.