• Journal of Information Management
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• v.42 no.2
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• pp.193-210
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• 2011

The study was applied to context window sizes and weighting method to obtain the best performance of word sense disambiguation using support vector machine. The context window sizes were used to a 3-word, sentence, 50-bytes, and document window around the targeted word. The weighting methods were used to Binary, Term Frequency(TF), TF ${\times}$ Inverse Document Frequency(IDF), and Log TF ${\times}$ IDF. As a result, the performance of 50-bytes in the context window size was best. The Binary weighting method showed the best performance.

• Journal of Internet Computing and Services
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• v.7 no.3
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• pp.169-178
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• 2006

In this paper, we define the large word sets which are noble variations the large item sets in mining association rules, and improve the accuracy for NaiveBayes based on the defined large word sets. In order to use them, a document is divided into the several paragraphs, and then each paragraph can be transformed as the transaction by extracting words in it. The proposed method was implemented by using Al:Categorizer framework and its accuracies were measured by the experiments for reuter-21578 data set. The results of the experiments show that the proposed method improves the accuracy of the conventional NaiveBayes.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.579-586
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• 2003

In this paper, we propose a novel approach to employ emotional features to document retrieval systems. Fine emotional features, such as HAPPY, SAD, ANGRY, FEAR, and DISGUST, have been used to represent Korean document. Users are allowed to use these features for retrieving their documents. Next, retrieved documents are learned by classification methods like cohesion factor, naive Bayesian, and, k-nearest neighbor approaches. In order to combine various approaches, voting method has been used. In addition, k-means clustering has been used for our experimentation. The performance of our approach proved to be better in accuracy than other methods, and be better in short texts rather than large documents.

• Journal of the Korea Safety Management & Science
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• v.10 no.4
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• pp.165-171
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• 2008

In the policy management system of the safety&health(same as the environment and the quality), the documentation of the management system is required in various international standards & guides. In case of 3 level classification of the system documents, it generally will be identified such name as the manual, the procedure(or the process) and the job instruction. Each level of the document has the unique role and usage though, the job instructions, especially, would be developed to support the practical job worker. Considering such document level, the job instructions should be reflect the latest know-how of the job-handling methods. And that, it should be used at any times by the related job worker. This paper is prepared for surveying the actual usage status of the job instructions of the domestic enterprises holding ISO certificate and trying to suggest the better way of application of the system documents.

• The Korean Journal of Archival Studies
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• no.2
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• pp.49-76
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• 2000

The government is pushing hard to realize the electronic and knowledge government. In connection with the efforts most public institutions have already started adopting the electronic document management system(EDMS), and have entered in a phase of modification of, and supplement to the system for the flow of the electronic documents among the agencies to be made possible by the month of November this year. The present situation is that the modification and supplement of EDMS are underway in the field that is unrelated to the records and archival management, what has been foreseen in the academic world of archival science. Under this circumstances the article places emphasis on the importance of the positive participation of the archivists in the development of the EDMS. To be concrete it reveals how the archival achievements can be practically applicable to the EDMS. What is particularly conspicuous in the article is the detailed description of how usefully control of record production, classification and description, evaluation and selection that are put into practice in the archival management law can be embodied in the development of the EDMS. Finally the article put emphasis on the positive exchanges and integration to bring the archival management science and computer science, archival management law and electronic government law, the archival management agencies and the agencies in charge of the electronic government together into whole to find a way for the methodical achievements of scientific archival management to be positively applicable to the electronic document management system(EDMS).

• Journal of Information Technology Services
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• v.18 no.2
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• pp.37-54
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• 2019

The purpose of this study is to present an evaluation model to measure the clarification level of stakeholder requirements of public sector software projects in the Republic of Korea. We tried to grasp the quality of proposal request through evaluation model. It also examines the impact of the level of stakeholder requirements on the level of system requirements. To do this, we analyzed existing research models and related standards related to business requirements and stakeholder requirements, and constructed evaluation models for the system operation concept documents in the ISO/IEC/IEEE 29148. The system operation concept document is a document prepared by organizing the requirements of stakeholders in the organization and sharing the intention of the organization. The evaluation model proposed in this study focuses on evaluating whether the contents related to the system operation concept are faithfully written in the request for proposal. The evaluation items consisted of three items: 'organization status', 'desired changes', and 'operational constraints'. The sample extracted 217 RFPs in the national procurement system. As a result of the analysis, the evaluation model proved to be valid and the internal consistency was maintained. The level of system operation concept was very low, and it was also found to affect the quality of system requirements. It is more important to clearly write stakeholders' requirements than the functional requirements. we propose a news classification methods for sentiment analysis that is effective for bankruptcy prediction model.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.624-629
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• 2022

에세이의 창의성을 자동으로 분류하는 기존의 주요 연구는 말뭉치에서 빈번하게 등장하지 않는 단어에 초점을 맞추어 기계학습을 수행한다. 그러나 이러한 연구는 에세이의 주제와 상관없이 단순히 참신한 단어가 많아 창의적으로 분류되는 문제점이 발생한다. 본 논문에서는 어텐션(Attention)과 역문서 빈도(Inverse Document Frequency; IDF)를 이용하여 에세이 내용 전달에 있어 중요하면서 참신한 단어에 높은 가중치를 두는 문맥 벡터를 구하고, 자기부호화기(AutoEncoder) 모델을 사용하여 문맥 벡터들로부터 창의적인 에세이와 창의적이지 않은 에세이의 특징 벡터를 추출한다. 그리고 시험 단계에서 새로운 에세이의 특징 벡터와 비교하여 그 에세이가 창의적인지 아닌지 분류하는 딥러닝 모델을 제안한다. 실험 결과에 따르면 제안 방안은 기존 방안에 비해 높은 정확도를 보인다. 구체적으로 제안 방안의 평균 정확도는 92%였고 기존의 주요 방안보다 9%의 정확도 향상을 보였다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.4
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• pp.587-596
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• 2024

Cyber Attack and Cyber Threat are getting confused and evolved. Therefore, using AI(Artificial Intelligence), which is the most important technology in Fourth Industry Revolution, to build a Cyber Threat Detection System is getting important. Especially, Government's SOC(Security Operation Center) is highly interested in using AI to build SOAR(Security Orchestration, Automation and Response) Solution to predict and build CTI(Cyber Threat Intelligence). In this thesis, We introduce the Cyber Threat Detection System by analyzing Network Traffic and Web Application Firewall(WAF) Log data. Additionally, we apply the well-known TF-IDF(Term Frequency-Inverse Document Frequency) method and AutoML technology to classify Web traffic attack type.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.221-241
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• 2018

As we enter the knowledge society, the importance of information as a new form of capital is being emphasized. The importance of information classification is also increasing for efficient management of digital information produced exponentially. In this study, we tried to automatically classify and provide tailored information that can help companies decide to make technology commercialization. Therefore, we propose a method to classify information based on Korea Standard Industry Classification (KSIC), which indicates the business characteristics of enterprises. The classification of information or documents has been largely based on machine learning, but there is not enough training data categorized on the basis of KSIC. Therefore, this study applied the method of calculating similarity between documents. Specifically, a method and a model for presenting the most appropriate KSIC code are proposed by collecting explanatory texts of each code of KSIC and calculating the similarity with the classification object document using the vector space model. The IPC data were collected and classified by KSIC. And then verified the methodology by comparing it with the KSIC-IPC concordance table provided by the Korean Intellectual Property Office. As a result of the verification, the highest agreement was obtained when the LT method, which is a kind of TF-IDF calculation formula, was applied. At this time, the degree of match of the first rank matching KSIC was 53% and the cumulative match of the fifth ranking was 76%. Through this, it can be confirmed that KSIC classification of technology, industry, and market information that SMEs need more quantitatively and objectively is possible. In addition, it is considered that the methods and results provided in this study can be used as a basic data to help the qualitative judgment of experts in creating a linkage table between heterogeneous classification systems.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.33-46
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• 2011

Since an e-mail has been an important mean of communication and information sharing, there have been much effort to classify e-mails efficiently by their contents. An e-mail has various forms in length and style, and words used in an e-mail are usually irregular. In addition, the criteria of an e-mail classification are subjective. As a result, it is quite difficult for the conventional text classification technique to be adapted to an e-mail classification efficiently. An e-mail classification technique in a commercial e-mail program uses a simple text filtering technique in an e-mail client. In the previous studies on automatic classification of an e-mail, the Naive Bayesian technique based on the probability has been used to improve the classification accuracy, and most of them are on an e-mail in English. This paper proposes the personalized recommendation technique of an email in Korean using a data mining technique of frequent patterns. The proposed technique consists of two phases such as the pre-processing of e-mails in an e-mail folder and the generating a profile for the e-mail folder. The generated profile is used for an e-mail to be classified into the most appropriate e-mail folder by the subjective criteria. The e-mail classification system is also implemented, which adapts the proposed technique.