• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.154-156
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• 2003

인터넷의 급속한 성장과 더불어 많은 정보와 데이터들을 인터넷을 통하여 얻을 수 있게 되었으며 많은 단체들이 문서들을 웹을 통하여 이용 가능하게 만들고 있다. 이에 따라 다양한 정보와 데이터를 효과적으로 분류하고 검색하는 문서 분류 (Document Classification)에 대한 알고리즘이 다양한 분야에서 널리 연구되어 왔으며 본 논문에서 초점을 두고 있는 전자 도서관 (Digital Library) 분야에서도 활발히 연구되어지고 있다. 하지만 기존의 전자 도서관의 문서 분류 알고리즘들은 문서들의 각 단락의 비중을 고려하지 않은 채 단어들의 발생 빈도에 초점을 두어 많은 잡음 단어 (Noise Term)를 포함하고 그로 인하여 분류 성능이 떨어졌다. 본 논문에서는 문서 단락의 중요도에 따라 다른 .가중치를 부여하여 단어 지지도 (Term Support)가 높은 단어들을 추출하고 그 단어들로 연관 규칙 (Association Rules)을 이용하여 분류 규칙을 생성하는 방법을 제안한다. 제안된 방법의 성능평가를 위해 문서 분류에 널리 쓰이는 나이브 베이지안 분류자 (Na$\square$ve Bayesian Classifier) 및 기존의 단순 연관 규칙 분류자 (Associative Classifier)와 비교 평가하였다. 그 결과, 각 가중치가 부여된 연관 규칙 분류 방법이 나이브 베이지안 분류 방법과 단순 연관 규칙 분류 방법보다 높은 성능을 보였다.

• The KIPS Transactions:PartB
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• v.8B no.1
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• pp.1-9
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• 2001

본 논문에서는 서식 문서를 짧은 처리 시간에 정확히 분류함으로써 실제 환경에서 응용할 수 있는 서식 분류 방법을 제안한다. 제안하는 방법은 서식 문서 이미지 전체를 다루기보다는 처리하고자 하는 서식 문서에서 서식 구조가 많이 다른 곳을 찾아서 매칭 영역으로 결정하고, 그 영역들에 대해서만 비교를 수행함으로써 계산 시간을 줄이고 인식률을 높인다. 선분 추출 시 오류를 고려하기 위하여 기존 인쇄 문자와 채워진 데이터, 그리고 매칭 영역의 크기 정보를 페널티 함수로 반영하여 매칭 영역 선택 시 고려한다. 본 방법은 구조적으로 많이 다르고, 양질의 특징을 포함하는 적은 수의 매칭 영역을 선택함으로써 처리 시간을 줄일 수 있음은 물론 높은 서식 분류율을 얻을 수 있다.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.106-116
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• 2021

To increase the quality of the system in the subjective-type question grading and document classification, we need the paragraph detection. But it is not easy because it is accompanied by semantic analysis. Many researches on the paragraph detection solve the detection problem using the word based clustering method. However, the word based method can not use the order and dependency relation between words. This paper suggests the paragraph detection system using syntactic-semantic relation between words with the Korean syntactic-semantic analysis. This system is the hybrid system of word based, concept based, and syntactic-semantic tree based detection. The experiment result of the system shows it has the better result than the word based system. This system will be utilized in Korean subjective question grading and document classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.3
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• pp.285-307
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• 2009

This paper introduces DICE, a Domain-Independent text Classification Engine. DICE is robust, efficient, and domain-independent in terms of software and architecture. Each module of the system is clearly modularized and encapsulated for extensibility. The clear modular architecture allows for simple and continuous verification and facilitates changes in multiple cycles, even after its major development period is complete. Those who want to make use of DICE can easily implement their ideas on this test bed and optimize it for a particular domain by simply adjusting the configuration file. Unlike other publically available tool kits or development environments targeted at general purpose classification models, DICE specializes in text classification with a number of useful functions specific to it. This paper focuses on the ways to locate the optimal states of a practical text classification framework by using various adaptation methods provided by the system such as feature selection, lemmatization, and classification models.

• Journal of Korea Multimedia Society
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• v.8 no.10
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• pp.1347-1359
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• 2005

We propose an efficient block classification of the document images using the second-order statistical texture features computed from spatial gray level dependence matrix (SGLDM). We studied on the techniques that will improve the block speed of the segmentation and feature extraction speed and the accuracy of the detailed classification. In order to speedup the block segmentation, we binarize the gray level image and then segmented by applying smoothing method instead of using texture features of gray level images. We extracted seven texture features from the SGLDM of the gray image blocks and we applied these normalized features to the BP (backpropagation) neural network, and classified the segmented blocks into the six detailed block categories of small font, medium font, large font, graphic, table, and photo blocks. Unlike the conventional texture classification of the gray level image in aerial terrain photos, we improve the classification speed by a single application of the texture discrimination mask, the size of which Is the same as that of each block already segmented in obtaining the SGLDM.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.263-266
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• 2009

Comments are short and not use spacing words or comma more than general document. We convert the 7-gram into 3-gram and select key features using topic signature. Topic signature is widely used for selecting features in document classification and summarization. We use the SVM(Support Vector Machines) as a classifier. From the result of experiments, we can see that the proposed method is outstanding over the previous methods. The proposed system can also apply to other languages.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.27-35
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• 2013

Recently, as Social Network Services(SNS) are becoming more popular, much research has been doing on analyzing public opinions from SNS. One of the most important tasks for solving such a problem is to separate opinion(subjective) documents from others(e.g. objective documents) in SNS. In this paper, we propose a new method of retrieving the opinion documents from Twitter. The reason why it is not easy to search or classify the opinion documents in Twitter is due to a lack of publicly available Twitter documents for training. To tackle the problem, at first, we build a machine-learned model for sentiment classification using the external documents similar to Twitter, and then modify the model to separate the opinion documents from Twitter. Experimental results show that proposed method can be applied successfully in opinion classification.

• Journal of Information Technology Services
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• v.18 no.4
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• pp.135-149
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• 2019

Recently, there are many researches for extracting meaningful information from large amount of text data. Among various applications to extract information from text, topic modeling which express latent topics as a group of keywords is mainly used. Topic modeling presents several topic keywords by term/topic weight and the quality of those keywords are usually evaluated through coherence which implies the similarity of those keywords. However, the topic quality evaluation method based only on the similarity of keywords has its limitations because it is difficult to describe the content of a topic accurately enough with just a set of similar words. In this research, therefore, we propose topic keywords reorganizing method to improve the identifiability of topics. To reorganize topic keywords, each document first needs to be labeled with one representative topic which can be extracted from traditional topic modeling. After that, classification rules for classifying each document into a corresponding label are generated, and new topic keywords are extracted based on the classification rules. To evaluated the performance our method, we performed an experiment on 1,000 news articles. From the experiment, we confirmed that the keywords extracted from our proposed method have better identifiability than traditional topic keywords.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.85-92
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• 2009

As World Wide Web is more popularized nowadays, the environment is flooded with the information through the web pages. However, despite such convenience of web, it is also creating many problems due to uncontrolled flood of information. The pornographic, violent and other harmful information freely available to the youth, who must be protected by the society, or other users who lack the power of judgment or self-control is creating serious social problems. To resolve those harmful words, various methods proposed and studied. This paper proposes and implements the protecting system that it protects internet youth user from harmful contents. To classify effective harmful/harmless contents, this system uses two step classification systems that is harmful word filtering and SVM learning based filtering. We achieved result that the average precision of 92.1%.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.22-26
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• 2005

In this paper, we classified the objectionable texts into four rates according to their harmfulness and proposed the hierarchical text rating system for objectionable documents. Since the documents in the same category have similarities in used words, expressions and structure of the document, the text rating system, which uses a single classification model, has low accuracy. To solve this problem, we separate objectionable documents into several subsets by using their properties, and then classify the subsets hierarchically. The proposed system consists of three layers. In each layer, we select features using the chi-square statistics, and then the weight of the features, which is calculated by using the TF-IDF weighting scheme, is used as an input of the non-linear SVM classifier. By means of a hierarchical scheme using the different features and the different number of features in each layer, we can characterize the objectionability of documents more effectively and expect to improve the performance of the rating system. We compared the performance of the proposed system and performance of several text rating systems and experimental results show that the proposed system can archive an excellent classification performance.