• Title/Summary/Keyword: text Categorization

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An Experimental Study on the Performance Improvement of Automatic Classification for the Articles of Korean Journals Based on Controlled Keywords in International Database (해외 데이터베이스의 통제키워드에 기초한 국내 학술지 논문의 자동분류 성능 향상에 관한 실험적 연구)

  • Kim, Pan Jun;Lee, Jae Yun
    • Journal of the Korean Society for Library and Information Science
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    • v.48 no.3
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    • pp.491-510
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    • 2014
  • As a major factor for efficient management and retrieval of the articles in databases, keywords are classified into uncontrolled keywords and controlled keywords. Most of Korean scholarly databases fail to provide controlled vocabularies to indexing research articles which help users to retrieve relevant papers exhaustively. In this paper, we carried out automatic descriptor assignment experiments to Korean articles using automatic classifiers learned with descriptors in international database. The results of the experiments show that the classifier learning with descriptors in international database can potentially offer controlled vocabularies to Korean scholarly articles having English s. Also, we sought to improve the performance of automatic descriptor assignment using various classifiers and combination of them.

Design of Automatic Document Classifier for IT documents based on SVM (SVM을 이용한 디렉토리 기반 기술정보 문서 자동 분류시스템 설계)

  • Kang, Yun-Hee;Park, Young-B.
    • Journal of IKEEE
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    • v.8 no.2 s.15
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    • pp.186-194
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    • 2004
  • Due to the exponential growth of information on the internet, it is getting difficult to find and organize relevant informations. To reduce heavy overload of accesses to information, automatic text classification for handling enormous documents is necessary. In this paper, we describe structure and implementation of a document classification system for web documents. We utilize SVM for documentation classification model that is constructed based on training set and its representative terms in a directory. In our system, SVM is trained and is used for document classification by using word set that is extracted from information and communication related web documents. In addition, we use vector-space model in order to represent characteristics based on TFiDF and training data consists of positive and negative classes that are represented by using characteristic set with weight. Experiments show the results of categorization and the correlation of vector length.

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A Study on Analysis of Topic Modeling using Customer Reviews based on Sharing Economy: Focusing on Sharing Parking (공유경제 기반의 고객리뷰를 이용한 토픽모델링 분석: 공유주차를 중심으로)

  • Lee, Taewon
    • Journal of Korea Society of Industrial Information Systems
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    • v.25 no.3
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    • pp.39-51
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    • 2020
  • This study will examine the social issues and consumer awareness of sharing parking through the method text mining. In this experiment, the topic by keyword was extracted and analyzed using TFIDF (Term frequency inverse document frequency) and LDA (Latent dirichlet allocation) technique. As a result of categorization by topic, citizens' complaints such as local government agreements, parking space negotiations, parking culture improvement, citizen participation, etc., played an important role in implementing shared parking services. The contribution of this study highly differentiated from previous studies that conducted exploratory studies using corporate and regional cases, and can be said to have a high academic contribution. In addition, based on the results obtained by utilizing the LDA analysis in this study, there is a practical contribution that it can be applied or utilized in establishing a sharing economy policy for revitalizing the local economy.

An Evaluation of Website Information Architecture for Old Adults: Focused on Organization and Labeling System (고령층을 위한 웹 사이트 정보 구조 평가: 조직화 체계와 레이블링 체계를 중심으로)

  • Seo, Jiwoong;Kim, Heesop
    • Journal of the Korean Society for information Management
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    • v.33 no.1
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    • pp.181-196
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    • 2016
  • The objective of this study is to evaluate the organization system and the labeling system of information architecture of a website for the elderly. To achieve this aims, we selected a representative website, i.e., Naver, and the participants were conducted given three types of search tasks using their own information literacy skills and they were answered to the questionnaire and an additional interview, if necessary. A total of 74 valid data were collected through the experiment, and we analyzed the data using SPSS Ver. 20. It revealed that Naver received a positive evaluation in the organization system aspect, particularly its systematic subject categorization and chronological browsing mechanisms. Old adults were preferred the icon-based labeling than the text-based labeling system, and showed a significant difference among their academic backgrounds.

An Analytical Study on Automatic Classification of Domestic Journal articles Based on Machine Learning (기계학습에 기초한 국내 학술지 논문의 자동분류에 관한 연구)

  • Kim, Pan Jun
    • Journal of the Korean Society for information Management
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    • v.35 no.2
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    • pp.37-62
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    • 2018
  • This study examined the factors affecting the performance of automatic classification based on machine learning for domestic journal articles in the field of LIS. In particular, In view of the classification performance that assigning automatically the class labels to the articles in "Journal of the Korean Society for Information Management", I investigated the characteristics of the key factors(weighting schemes, training set size, classification algorithms, label assigning methods) through the diversified experiments. Consequently, It is effective to apply each element appropriately according to the classification environment and the characteristics of the document set, and a fairly good performance can be obtained by using a simpler model. In addition, the classification of domestic journals can be considered as a multi-label classification that assigns more than one category to a specific article. Therefore, I proposed an optimal classification model using simple and fast classification algorithm and small learning set considering this environment.

An Analytical Study on Performance Factors of Automatic Classification based on Machine Learning (기계학습에 기초한 자동분류의 성능 요소에 관한 연구)

  • Kim, Pan Jun
    • Journal of the Korean Society for information Management
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    • v.33 no.2
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    • pp.33-59
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    • 2016
  • This study examined the factors affecting the performance of automatic classification for the domestic conference papers based on machine learning techniques. In particular, In view of the classification performance that assigning automatically the class labels to the papers in Proceedings of the Conference of Korean Society for Information Management using Rocchio algorithm, I investigated the characteristics of the key factors (classifier formation methods, training set size, weighting schemes, label assigning methods) through the diversified experiments. Consequently, It is more effective that apply proper parameters (${\beta}$, ${\lambda}$) and training set size (more than 5 years) according to the classification environments and properties of the document set. and If the performance is equivalent, I discovered that the use of the more simple methods (single weighting schemes) is very efficient. Also, because the classification of domestic papers is corresponding with multi-label classification which assigning more than one label to an article, it is necessary to develop the optimum classification model based on the characteristics of the key factors in consideration of this environment.

Comparison of Zhusang Between as Discovered in a Medical Book Excavated in China and Other Classical Books (중국 출토의서에 보이는 '제상(諸傷)'과 전래문헌의 비교 고찰)

  • Lee, Kyung
    • Journal of Korean Medical classics
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    • v.31 no.4
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    • pp.17-26
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    • 2018
  • Objectives : This paper studies Zhushang, which is the name of a disease found in excavated books. Zhusang is the first disease listed in Wushierbingfang, which is a medical textbook excavated at Mawangdui, and Zhusang was followed by diseases such as Jinshang and Renshang. The paper studies what disease each of the word is refering to in terms of graphonomy, and compared the difference of their treatment from other classical texts. Methods : The scope of the study of this paper includes the excavated textbooks that seem to contain any disease related to Zhusang, and the two major text books of these are Wushierbingfang and Wuweihandaiyijian. Then Shennongbencao jing, which is the one of the earlier books on herbology, and Bencao gangmu, which was written based on the former, wer used to make comparisons. Parts in Donguibogam that seem to be related to the parts in the excavated texts were also compared. The study was done by first performing historical research on the names of the diseases in the excavated books, and compared them with the contents of the classical texts. Results : The Zhushang discovered in Wushierbingfang refers to wounds caused by metal or wood. It was interesting how they created a word for diseases depending on the cause. Only Jinshang is found in Wuweihandaiyijian, and the fact that different causes gave way to different names tells us that they had corresponding treatment. The categorization of Zhushang, Jinshang, and Renshang is corresponded better in Donguibogam than Chinese medical books.

An Experimental Study on the Automatic Classification of Korean Journal Articles through Feature Selection (자질선정을 통한 국내 학술지 논문의 자동분류에 관한 연구)

  • Kim, Pan Jun
    • Journal of the Korean Society for information Management
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    • v.39 no.1
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    • pp.69-90
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    • 2022
  • As basic data that can systematically support and evaluate R&D activities as well as set current and future research directions by grasping specific trends in domestic academic research, I sought efficient ways to assign standardized subject categories (control keywords) to individual journal papers. To this end, I conducted various experiments on major factors affecting the performance of automatic classification, focusing on feature selection techniques, for the purpose of automatically allocating the classification categories on the National Research Foundation of Korea's Academic Research Classification Scheme to domestic journal papers. As a result, the automatic classification of domestic journal papers, which are imbalanced datasets of the real environment, showed that a fairly good level of performance can be expected using more simple classifiers, feature selection techniques, and relatively small training sets.

Improving Classification Accuracy in Hierarchical Trees via Greedy Node Expansion

  • Byungjin Lim;Jong Wook Kim
    • Journal of the Korea Society of Computer and Information
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    • v.29 no.6
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    • pp.113-120
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    • 2024
  • With the advancement of information and communication technology, we can easily generate various forms of data in our daily lives. To efficiently manage such a large amount of data, systematic classification into categories is essential. For effective search and navigation, data is organized into a tree-like hierarchical structure known as a category tree, which is commonly seen in news websites and Wikipedia. As a result, various techniques have been proposed to classify large volumes of documents into the terminal nodes of category trees. However, document classification methods using category trees face a problem: as the height of the tree increases, the number of terminal nodes multiplies exponentially, which increases the probability of misclassification and ultimately leads to a reduction in classification accuracy. Therefore, in this paper, we propose a new node expansion-based classification algorithm that satisfies the classification accuracy required by the application, while enabling detailed categorization. The proposed method uses a greedy approach to prioritize the expansion of nodes with high classification accuracy, thereby maximizing the overall classification accuracy of the category tree. Experimental results on real data show that the proposed technique provides improved performance over naive methods.

Research on Multi-facted News Article Classification Models Classifying Subjects, Geographies and Genres (심층 주제, 지역, 장르를 모두 분류할 수 있는 다면적 뉴스 기사 자동 분류 모델 연구)

  • Hyojin Lee;SungPil Choi
    • Journal of the Korean Society for Library and Information Science
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    • v.58 no.3
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    • pp.65-89
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    • 2024
  • This study developed a model to classify news articles into categories of topic, genre, and region using a Korean Pre-trained Language model. To achieve this, a new news article classification system was designed by referring to the classification systems of domestic media outlets. The topic and genre classification models were implemented as hierarchical classification models that link the main categories and subcategories, and their performance was compared with that of an integrated category model. The evaluation results showed that the hierarchical structure classification model had the advantage of providing more precise categorization in ambiguous or overlapping categories compared to the integrated category model. For regional classification of news articles, a model was built to classify into 18 categories, and for regional news articles, the regional characteristics were clearly reflected in the text, resulting in high performance. This study demonstrated the effectiveness of classifying news articles from multiple perspectives-topic, genre, and region-and emphasized the significance of suggesting the potential for a multi-dimensional news article classification service that meets user needs.