• Journal of Intelligence and Information Systems
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• v.8 no.1
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• pp.27-39
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• 2002

A locating text technique capable of locating and extracting text blocks in various Web images is presented here. Until now this area of work has been ignored by researchers even if this sort of text may be meaningful for internet users. The algorithms associated with the technique work without prior knowledge of the text orientation, size or font. In the work presented in this research, our text extraction algorithm utilizes useful edge detection followed by histogram analysis on the genuine characteristics of letters defined by text clustering region, to properly perform extraction of the text region that does not depend on font styles and sizes. By a number of experiments we have showed impressively acceptable results.

• Journal of Advanced Navigation Technology
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• v.12 no.5
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• pp.502-508
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• 2008

With the increased use of the internet and the tremendous amount of data it transfers, it is more necessary to summarize documents. We propose a new method using the Non-negative Semantic Variable Matrix (NSVM) and the generic important of semantic features obtained by Non-negative Matrix Factorization (NMF) to extract the sentences for automatic generic summarization. The proposed method use non-negative constraints which is more similar to the human's cognition process. As a result, the proposed method selects more meaningful sentences for summarization than the unsupervised method used the Latent Semantic Analysis (LSA) or clustering methods. The experimental results show that the proposed method archives better performance than other methods.

• Journal of Korean Library and Information Science Society
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• v.39 no.4
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• pp.283-304
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• 2008

This is to develop Intelligent Retrieval System which can automatically present early query's category terms(association terms connected with knowledge structure of relevant terminology) through learning function and it changes searching form automatically and runs it with association terms. For the reason, this theoretical study of Intelligent Automatic Indexing System abstracts expert's index term through learning and clustering algorism about automatic classification, text mining(categorization), and document category representation. It also demonstrates a good capacity in the aspects of expense, time, recall ratio, and precision ratio.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.8
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• pp.1213-1225
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• 1993

Hypermedia systems have the browsing mechanism using links and provide navigation tools to retrieve and represent multimedia information. In this study we designed and implemented a hypermedia system which has the hierarchical group and local map for effective navigation. We also propose the clustering mechanism which constructs a cluster tree and uses this knowledge for navigation. The system has been designed to integrate the browsing and searching function of the hypermedia system for efficient multimedia information retrieval and user-interface. This system can be used to develop hypermedia application systems in the area of encyclopedia, reference document information, electronic dictionary and electronic book.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.132-137
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• 2018

뉴스 기반의 실시간 이슈 분석을 위해서는 실시간으로 생성되는 다중 뉴스 기사 집합을 입력으로 받아 점증적으로 군집화 하고, 각 군집별 정보를 자동으로 요약하는 기술이 필요하다. 기존에는 정적인 데이터 기반의 군집화와 요약 각각에 대한 연구는 활발히 진행되고 있지만, 실시간으로 입력되는 대량의 데이터를 위한 점증적인 군집화와 요약에 대한 연구는 매우 부족하다. 따라서 본 논문에서는 실시간으로 입력되는 대량의 뉴스 기사 집합을 분석하기 위한 점증적이고 계층적인 뉴스 군집화 및 다중 문서 요약 방법을 제안한다. 평가를 위해서 2016년 10월, 11월 두 달간의 실제 데이터를 사용 하였으며, 전문 교육을 받은 연구원들이 Precision at k 기반의 정성평가를 진행하였다. 그 결과, 자동으로 생성된 12개의 군집에서 군집 성능은 평균 66% (상위계층 $l_1$: 82%, 하위계층 $l_2$: 43%), 요약 성능은 평균 92%를 얻었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.26-29
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• 2016

웹 문서 추천 시스템에서는 유사한 내용의 문서임에도 불구하고 URL이 달라서 다른 문서로 인식하여 사용자에게 추천하는 데이터 희소성 문제가 있다. 여기서 기존 연구들은 이 문제에 대한 해결 방법으로 TF-IDF를 이용하였으나 비용 및 시간의 한계가 있으며 유의어 분류 문제가 있다. 본 논문에서는 Word2Vec을 이용한 웹문서 학습 시스템을 통해 문제를 해결한다. 제안 시스템은 언론사의 뉴스를 수집하고 이를 정형화된 형식으로 분석하여 가공하는 전처리 과정을 거친 후 Word2Vec 학습을 통해 문서 벡터를 생성하고 이를 K-Means 클러스터링으로 유사 문서군으로 분류한다. 이 시스템을 이용하면 데이터 희소성 문제를 해결할 뿐만 아니라 연산량이 TF-IDF에 비해 줄어들고 유의어 분류 시 유사도가 높아지는 강점이 있다.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1254-1271
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• 2018

Keyphrase extraction is one of fundamental natural language processing (NLP) tools to improve many text-mining applications such as document summarization and clustering. In this paper, we propose to use two novel techniques on the top of the state-of-the-art keyphrase extraction methods. First is the anti-patterns that aim to recognize non-keyphrase candidates. The state-of-the-art methods often used the rich feature set to identify keyphrases while those rich feature set cover only some of all keyphrases because keyphrases share very few similar patterns and stylistic features while non-keyphrase candidates often share many similar patterns and stylistic features. Second one is to use the dependency graph instead of the word co-occurrence graph that could not connect two words that are syntactically related and placed far from each other in a sentence while the dependency graph can do so. In experiments, we have compared the performances with different settings of the graphs (co-occurrence and dependency), and with the existing method results. Finally, we discovered that the combination method of dependency graph and anti-patterns outperform the state-of-the-art performances.

• Journal of Information Technology Applications and Management
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• v.26 no.3
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• pp.43-59
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• 2019

The goal of this paper is to investigate changes in North Korea's domestic and foreign policies through automated text analysis over North Korean new year addresses, one of most important and authoritative document publicly announced by North Korean government. Based on that data, we then analyze the status of text mining research, using a text mining technique to find the topics, methods, and trends of text mining research. We also investigate the characteristics and method of analysis of the text mining techniques, confirmed by analysis of the data. We propose a procedure to find meaningful tendencies based on a combination of text mining, cluster analysis, and co-occurrence networks. To demonstrate applicability and effectiveness of the proposed procedure, we analyzed the inaugural addresses of Kim Jung Un of the North Korea from 2017 to 2019. The main results of this study show that trends in the North Korean national policy agenda can be discovered based on clustering and visualization algorithms. We found that uncovered semantic structures of North Korean new year addresses closely follow major changes in North Korean government's positions toward their own people as well as outside audience such as USA and South Korea.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04a
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• pp.519-522
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• 2002

일반적으로 PubMed와 같은 인터넷을 이용한 대규모 의료 문헌정보 검색시스템에서 포괄적인 주제어나 간결한 주제어를 이용한 검색을 시도할 경우, 종종 매우 다양한 세부주제의 문헌리스트들이 다량으로 검색된다. 이러한 경우 이용자는 실제로 본인이 원했던 세부주제에 부합되는 문헌들을 찾기 위해서는 검색결과로 주어진 긴 문헌리스트상의 문헌 하나하나에 대해 다시 문헌제목이나 혹은 요약 등의 내용을 직접 읽어보고 내용을 확인하여야 한다. 이러한 작업은 매우 번거럽고 시간과 노력을 많이 필요로 한다. 따라서 본 논문에서는 이러한 노력을 줄이기 위한 한 가지 방안으로, PubMed 시스템의 주제어 검색결과로 주어진 문헌들에 대해 내용의 유사성과 차별성에 따라 자동으로 몇 개의 그룹으로 나누어주는 군집화시스템 MedCluster의 설계와 구현에 대해 소개한다. MedCluster의 큰 특징은 기존의 문서 군집화 방법과는 다른 신경망 GHSOM을 이용한 군집화 방법을 사용하는 점이다. GHSOM은 미리 문서 그룹의 개수를 정해줄 필요가 없고 다양한 레벨의 문서 그룹들을 얻을 수 있는 계층적 군집화를 이루어낸다는 장점을 가지고 있다. 본 논문에서는 신경망 GHSOM의 구조와 특성에 대해 간략히 살펴보고, GHSOM을 채용한 의료문헌 군집화시스템 MedCluster의 설계와 구현에 대해 설명한다.

• The Journal of Information Technology
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• v.1 no.1
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• pp.13-26
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• 1998

In general Information retrieval systems, improper keywords are often extracted and different search results are offered comparing to user's aim bacause the systems use only term frequency informations for selecting keywords and don't consider their meanings. It represents that improving precision is limited without considering semantics of keywords because recall ratio and precision have inverse proportion relation. In this paper, a system which is able to improve precision without decreasing recall ratio is designed and implemented, as client user module is introduced which can send feedbacks to server with user's intention. For this purpose, keywords are selected using relative term frequency and inverse document frequency and co-occurrence words are extracted from original documents. Then, the keywords are clustered by their semantics using calculated mutual informations. In this paper, the system can reject inappropriate documents using segmented semantic informations according to feedbacks from client user module. Consequently precision of the system is improved without decreasing recall ratio.