• Proceedings of the Korean Printing Society Conference
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• 2001.06a
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• pp.63-77
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• 2001

PDF(Portable Document Format) is a file format that Adobe advances postscritp technique and use in managing document information or electric publishing(internet, CD-ROM, DVD). PDF is a devised document type for being able to read and print anywhere, independent of OS, printer type, resolution, and the kind of computer etc. Because this includes a compressing function, it transfers document through a small size of file in internet or intranet. In addition, that is a file format has various advantages-sharing of information and transfering documents in on line or off line environment. In this paper, we developed electronic document system using PDF format. Electronic document system consists of filter, automatic indexing, special searching system and web server. The information used in this paper is database made using Zwon\`s DocuCom. The filter recognizes various kinds of document structure. And according to property of document, it produces ASCII output. In addition to processing various formats of document, the filter can extract keywords in documents of MS WORD, Excel, Powerpoint, PDF, CAD etc. This filter uses the structure of window printer drive and can extract the information for text, page, font type and size from relevant document. The automatic indexing recognizes the formatted tag of document form ASCII text produced by filter and extracts adequate keyword to structure and property of document. PDF electronic document systems proposed in this paper can be used in Internet, PC communication. Users can choose and read electronic documents by two ways. First, users can choose and read relevant books using PDF electronic document homepage. Second, users can use PDF integrated-search system. User can search after inputing keyword and choose reference field and type of data. But, now, PDF products of Adobe can\`t support the Korean character. If this problem is resolved, we thick that PDF applications system looks active. Although there is limited function in case of using Zwon DocuCom used in this study, we think that there isn\`t a great deal of difficulty in electronic document and building digital database.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.837-843
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• 2001

In this work, we propose a new method of extracting and weighting representative keywords(RKs) from a few documents that might interest a user. In order to extract RKs, we first extract candidate terms and them choose a number of terms called initial representative keywords (IRKs) from them through fuzzy inference. Then, by expanding and reweighting IRKs using term co-occurrence similarity, the final RKs are obtained. Performance of our approach is heavily influenced by effectiveness of selection method of IRKs so that we choose fuzzy inference because it is more effective in handling the uncertainty inherent in selecting representative keywords of documents. The problem addressed in this paper can be viewed as the one of calculating center of document vectors. So, to show the usefulness of our approach, we compare with two famous methods - Rocchio and Widrow-Hoff - on a number of documents collections. The result show that our approach outperforms the other approaches.

• Journal of KIISE
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• v.44 no.5
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• pp.522-536
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• 2017

Keyword extraction can be utilized in text mining of massive documents for efficient extraction of subject or related words from the document. In this study, we proposed a hierarchical graph model based on the co-occurrence relationship, the intrinsic dependency relationship between words, and common sub-word in a single document. In addition, the enhanced TextRank algorithm that can reflect the influences of outgoing edges as well as those of incoming edges is proposed. Subsequently a novel keyword extraction scheme using the proposed hierarchical graph model and the enhanced TextRank algorithm is proposed to extract representative keywords from a single document. In the experiments, various evaluation methods were applied to the various subject documents in order to verify the accuracy and adaptability of the proposed scheme. As the results, the proposed scheme showed better performance than the previous schemes.

• Journal of Internet Computing and Services
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• v.19 no.4
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• pp.71-82
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• 2018

As the era of the $4^{th}$ industrial revolution began, it is possible to provide smart education by utilizing various new technologies. This paper included 6 steps to prove that the educational satisfaction of students' can be improved by applying the technology of the $4^{th}$ Industrial Revolution toward smart education. The first step is to review technologies of the $4^{th}$ industrial revolution that could enable smart education. The second step is to define areas that smart education should include by adopting technologies of the $4^{th}$ industrial revolution. The third step is to extract the keyword through literature review while the keyword can constitute the smart education for the defined areas. The fourth step is to present the research model by using the extracted keyword. The fifth step is to verify the proposed research model through questionnaires. The last step is to analyze the result of questionnaires to suggest better educational method. Consequentially, the purpose of this study is to verify the effectiveness of smart education by measuring students' expectation about smart education through questionnaire. As a result, students responded that the presented factors of smart education could maximize the effect of education by increasing the satisfaction of education. Therefore, it is necessary to utilize the technology of the $4^{th}$ Industrial revolution in the education field and apply the smart education method for better education.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.1-10
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• 2018

Current evaluation practices for IT projects suffer from several problems, which include the difficulty of self-explanation for the evaluation results and the improperly scaled scoring system. This study aims to develop a methodology of opinion mining to extract key factors for the causal relationship analysis and to assess the feasibility of quantifying evaluation scores from text comments using opinion mining based on big data analysis. The research has been performed on the domain of publicly procured IT proposal evaluations, which are managed by the National Procurement Service. Around 10,000 sets of comments and evaluation scores have been gathered, most of which are in the form of digital data but some in paper documents. Thus, more refined form of text has been prepared using various tools. From them, keywords for factors and polarity indicators have been extracted, and experts on this domain have selected some of them as the key factors and indicators. Also, those keywords have been grouped into into dimensions. Causal relationship between keyword or dimension factors and evaluation scores were analyzed based on the two research models-a keyword-based model and a dimension-based model, using the correlation analysis and the regression analysis. The results show that keyword factors such as planning, strategy, technology and PM mostly affects the evaluation result and that the keywords are more appropriate forms of factors for causal relationship analysis than the dimensions. Also, it can be asserted from the analysis that evaluation scores can be composed or calculated from the unstructured text comments using opinion mining, when a comprehensive dictionary of polarity for Korean language can be provided. This study may contribute to the area of big data-based evaluation methodology and opinion mining for IT proposal evaluation, leading to a more reliable and effective IT proposal evaluation method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.8
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• pp.1049-1054
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• 2018

Keyword searching used in the past as a method of finding similar patents, and automated classification by machine learning is using in recently. Keyword searching is a method of analyzing data that is formalized through data refinement. While the accuracy for short text is high, long one consisted of several words like as document that is not able to analyze the meaning contained in sentences. In semantic analysis level, the method of automatic classification is used to classify sentences composed of several words by unstructured data analysis. There was an attempt to find similar documents by combining the two methods. However, it have a problem in the algorithm w the methods of analysis are different ways to use simultaneous unstructured data and regular data. In this paper, we study the method of extracting keywords implied in the document and using the LDA(Latent Semantic Analysis) method to classify documents efficiently without human intervention and finding similar patents.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.333-342
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• 2021

In this study, we analyzed the vocabulary, lemmas, keywords, and n-grams in 『Treasure Island』 to identify certain linguistic features of this British-American children's adventure novel. The current study found that, contrary to the popular claim that frequently-used words are important and essential to a story, the set of frequently-used words in 『Treasure Island』 were mostly function words and proper nouns that were not directly related to the plot found in 『Treasure Island』. We also ascertained that a list of keywords using a statistical method making use of a corpus program was not good enough to surmise the story of 『Treasure Island』. However, we managed to extract 30 keywords through the first quantitative keyword analysis and then a second qualitative keyword analysis. We also carried out a series of n-gram analyses and were able to discover lexical bundles that were preferred and frequently used by the author of 『Treasure Island』. We hope that the results of this study will help spread this knowledge among British-American children's literature as well as to further put forward corpus stylistic theory.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.35-46
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• 2023

In this study, we propose a Dual Approach methodology to enhance the accuracy of document classifiers by utilizing both contextual and keyword information. Firstly, contextual information is extracted using Google's BERT, a pre-trained language model known for its outstanding performance in various natural language understanding tasks. Specifically, we employ KoBERT, a pre-trained model on the Korean corpus, to extract contextual information in the form of the CLS token. Secondly, keyword information is generated for each document by encoding the set of keywords into a single vector using an Autoencoder. We applied the proposed approach to 40,130 documents related to healthcare and medicine from the National R&D Projects database of the National Science and Technology Information Service (NTIS). The experimental results demonstrate that the proposed methodology outperforms existing methods that rely solely on document or word information in terms of accuracy for document classification.

• Journal of the Korean Geosynthetics Society
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• v.22 no.2
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• pp.47-54
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• 2023

In this paper, we propose the technique of optimal search keyword extraction and retrieval for news article classification. The proposed technique was verified as an example of identifying trends related to North Korean construction. A representative Korean media platform, BigKinds, was used to select sample articles and extract keywords. The extracted keywords were vectorized using word embedding and based on this, the similarity between the extracted keywords was examined through cosine similarity. In addition, words with a similarity of 0.5 or higher were clustered based on the top 10 frequencies. Each cluster was formed as 'OR' between keywords inside the cluster and 'AND' between clusters according to the search form of the BigKinds. As a result of the in-depth analysis, it was confirmed that meaningful articles appropriate for the original purpose were extracted. This paper is significant in that it is possible to classify news articles suitable for the user's specific purpose without modifying the existing classification system and search form.

• Journal of the Korean Society for information Management
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• v.36 no.4
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• pp.7-19
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• 2019

The aim of this study is to develop a technique for keyword extraction in Korean-Chinese text in the modern period. We considered a Korean morphological analyzer and a particle in classical Chinese as a possible method for this study. We applied our method to the journal "Sisachongbo," employing proper-noun dictionaries and a list of stop words to extract index terms. The results show that our system achieved better performance than a Chinese morphological analyzer in terms of recall and precision. This study is the first research to develop an automatic indexing system in the traditional Korean-Chinese mixed text.