• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.695-697
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• 2010

문서에 포함되는 어휘는 문서 분류의 정보를 가지므로 문서를 분석하여 유용한 단어를 추출하는 것은 다양한 서비스와 연계되어 사용될 수 있어 매우 유용한 일이다. 문서 자동 분류에서는 분류자질 선정 방식에 따라 분류정확도가 서로 달라질 수 있으며, 문서에서 추출되는 유용한 단어에 따라 인지되는 분야가 달라질 수 있다. 이에 본 논문에서는 각 문서에 포함되는 단어에 대한 카이제곱 통계량 점수를 사용하여 단어별 문서 분류에 대한 단어의 자질을 평가하고 문서의 분류별 유용한 단어를 자동 추출하는 방법을 제안하고 개발한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.665-668
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• 2004

최근의 웹 문서는 텍스트뿐만 아니라 이미지, 사운드 등 다른 여러 형태로 표현되고 있어서 텍스트의 비중이 낮아지고 있다. 그래서 문서 내에서 일정량 이상의 단어 추출이 어려운 문서들에 대해서 기존의 단어 정보만을 이용한 문서 범주화 방법은 좋은 성능을 기대할 수 없다. 그래서 본 논문은 Anchor Text 단어 정보의 자질 적합성 판단에 의한 새로운 자동 문서 범주화 모델을 제안한다. 문서 범주화 모델로는 베이지언 확률 모델을 이용하였으며, 카이제곱 통계량을 사용하여 자질을 선정하였다. 문서 내에서 추출된 단어 자질들이 해당 문서를 판단하는데 부족하다고 판단되면 문서의 링크정보를 이용하여 연결된 문서의 단어 자질과 Anchor Text의 단어 자질을 반영함으로써 성능을 향상시킨다.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.144-151
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• 2021

딥러닝의 등장으로 자동 음성 인식 (Automatic Speech Recognition) 기술은 인간과 컴퓨터의 상호작용을 위한 가장 중요한 요소로 자리 잡았다. 그러나 아직까지 유사 발음 오류, 띄어쓰기 오류, 기호부착 오류 등과 같이 해결해야할 난제들이 많이 존재하며 오류 유형에 대한 명확한 기준 정립이 되고 있지 않은 실정이다. 이에 본 논문은 음성 인식 시스템의 오류 유형 분류 기준을 한국어에 특화되게 설계하였으며 이를 다양한 상용화 음성 인식 시스템을 바탕으로 질적 분석 및 오류 분류를 진행하였다. 실험의 경우 도메인과 어투에 따른 분석을 각각 진행하였으며 이를 통해 각 상용화 시스템별 강건한 부분과 약점인 부분을 파악할 수 있었다.

• 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.

• 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.

• Journal of Korea Multimedia Society
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• v.4 no.2
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• pp.171-181
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• 2001

The previous Bayesian document categorization method has problems that it requires a lot of time and effort in word clustering and it hardly reflects the semantic information between words. In this paper, we propose a weighted Bayesian document categorizing method based on association word knowledge base acquired by mining technique. The proposed method constructs weighted association word knowledge base using documents in training set. Then, classifier using Bayesian probability categorizes documents based on the constructed association word knowledge base. In order to evaluate performance of the proposed method, we compare our experimental results with those of weighted Bayesian document categorizing method using vocabulary dictionary by mutual information, weighted Bayesian document categorizing method, and simple Bayesian document categorizing method. The experimental result shows that weighted Bayesian categorizing method using association word knowledge base has improved performance 0.87% and 2.77% and 5.09% over weighted Bayesian categorizing method using vocabulary dictionary by mutual information and weighted Bayesian method and simple Bayesian method, respectively.

• Journal of the Korean Society for information Management
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• v.37 no.1
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• pp.1-21
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• 2020

This study applied automatic classification using table of contents (TOC) text for 6,253 social science books from a newly arrived list collected by a university library. The k-nearest neighbors (kNN) algorithm was used as a classifier, and the ten divisions on the second level of the DDC's main class 300 given to books by the library were used as classes (labels). The features used in this study were keywords extracted from titles and TOCs of the books. The TOCs were obtained through the OpenAPI from an Internet bookstore. As a result, it was found that the TOC features were good for improving both classification recall and precision. The TOC was shown to reduce the overfitting problem of imbalanced data with its rich features. Law and education have high topic specificity in the field of social sciences, so the only title features can bring good classification performance in these fields.

• The Journal of Society for e-Business Studies
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• v.22 no.2
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• pp.61-77
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• 2017

Recently, the number of academic conference information on the Internet has rapidly increased, the automatic classification of academic conference information according to research subjects enables researchers to find the related academic conference efficiently. Information provided by most conference listing services is limited to title, date, location, and website URL. However, among these features, the only feature containing topical words is title, which causes information insufficiency problem. Therefore, we propose methods that aim to resolve information insufficiency problem by utilizing web contents. Specifically, the proposed methods the extract main contents from a HTML document collected by using a website URL. Based on the similarity between the title of a conference and its main contents, the topical keywords are selected to enforce the important keywords among the main contents. The experiment results conducted by using a real-world dataset showed that the use of additional information extracted from the conference websites is successful in improving the conference classification performances. We plan to further improve the accuracy of conference classification by considering the structure of websites.

• 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.

• Journal of KIISE:Software and Applications
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• v.30 no.11
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• pp.1062-1071
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• 2003

A genre-based classification means classifying documents by the purpose for which they were written, not by the semantics or subject areas. Most genre classifying methods in the past were based on the existing documents categorization algorithms and ineffective for feature selections, resulting in low quality classification results. In this research, we propose a new method for automatic classification of digital documents by genre. The genre classifier we developed uses the deviation statistic between the genre-revealing term frequencies and between the subject-revealing term frequencies within a genre. We collected Web documents to evaluate the proposed genre classification method. The experimental results show that the proposed method outperforms a direct application of a kai-square feature selection and bayesian classifier often used for subject classification by proving an excellent accuracy of about 30 percent.