• The KIPS Transactions:PartD
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• v.19D no.2
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• pp.133-138
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• 2012

Queries entered in a search box are the results of users' activities to actively seek information. Therefore, search logs are important data which represent users' information needs. The purpose of this study is to examine if there is a relationship between the results of queries automatically classified and the categories of documents accessed. Search sessions were identified in 2009 NDSL(National Discovery for Science Leaders) log dataset of KISTI (Korea Institute of Science and Technology Information). Queries and items used were extracted by session. The queries were processed using an automatic classifier. The identified queries were then compared with the subject categories of items used. As a result, it was found that the average similarity was 58.8% for the automatic classification of the top 100 queries. Interestingly, this result is a numerical value lower than 76.8%, the result of search evaluated by experts. The reason for this difference explains that the terms used as queries are newly emerging as those of concern in other fields of research.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.343-344
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• 2023

과학기술 문헌을 활용한 계량정보분석에서 학과정보의 활용은 매유 유용하다. 본 논문에서는 한국과학기술인용색인데이터베이스에 등재된 국내 학술지 논문에 출현하는 대학기관 소속 저자의 학과정보를 추출하고 데이터 정제 및 학과유형 분류 처리를 통해 학과정보 데이터셋을 구축하였다. 학과정보 데이터셋을 학습데이터와 검증데이터로 이용하여 딥러닝 기반의 자동분류 모델을 구현하였으며, 모델 성능 평가 결과는 한글 학과정보 기준 98.6%와 영문 학과정보 기준 97.6%의 정확률로 측정되었다. 향후 과학기술 분야별 지적관계 분석 및 논문 주제분류 등에 학과정보 자동분류 처리기의 활용이 기대된다.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.1
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• pp.73-81
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• 2010

In recent years, there have been diverse researches and utilizations of creating geo-spatial information with high resolution satellite images. However thematic maps made with middle or low resolution satellite images have low location accuracy and precision of thematic information. This study set out to propose a method of making a precision thematic map with high resolution satellite images by examining the conversion from the conventional method based on middle or low resolution satellite images to the automatic method based on high resolution satellite images of GSD 1m or lower, extracting thematic information of middle or large scale of 1/5,000 or lower, and analyzing its accuracy. Seven classification classes were categorized according to the object-oriented classification in order to automatically extract thematic information with high resolution satellite images. And the classification results were compared and analyzed with the old middle scale land cover map and 1/1000 digital map.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.2
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• pp.47-55
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• 2008

The purpose of this paper is to develop an analytical methodology of or릴 documents by the application of. Information Technologies. This system consists of the key word search, contents summary, clustering, classification & topic tracing of the contents. The integrated model of the five levels of retrieval technologies can be exhaustively used in the analysis of oral documents, which were collected as oral history of five men and women in the area of North Jeolla. Of the five methods topic tracing is the most pioneering accomplishment both home and abroad. In final this research will shed light on the methodological and theoretical studies of oral history and culture.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.869-872
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• 2017

정보검색(Information retrieval) 및 텍스트 분석을 위해 수집하는 비정형 데이터 즉, 자연어를 전처리하는 과정 중 하나인 불용어(Stopword) 제거는 모델의 품질을 높일 수 있는 쉽고, 효과적인 방법 중에 하나이다. 특히 다양한 텍스트 문서에 잠재된 주제를 추출하는 기법인 토픽모델링의 경우, 너무 오래되거나, 수집된 문서의 도메인이나 성격과 무관한 불용어의 제거로 인해, 해당 토픽 모델에서 학습되어 생성된 주제 관련 단어들의 일관성이 떨어지게 된다. 따라서 분석가가 분류된 주제를 올바르게 해석하는데 있어 많은 어려움이 따르게 된다. 본 논문에서는 이러한 문제점을 해결하기 위해 일반적으로 사용되는 표준 불용어 대신 관련 도메인 문서로부터 추출되는 점별 상호정보량(PMI: Pointwise Mutual Information)을 이용하여 불용어를 자동으로 생성해주는 기법을 제안한다. 생성된 불용어와 표준 불용어를 통해 토픽 모델의 품질을 혼잡도(Perplexity)로써 측정한 결과, 본 논문에서 제안한 기법으로 생성한 30개의 불용어가 421개의 표준 불용어보다 더 높은 모델 성능을 보였다.

• The KIPS Transactions:PartB
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• v.11B no.5
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• pp.555-562
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• 2004

A genre or a style is another view of documents different from a subject or a topic. The style is also a criterion to classify the documents. There have been several studies on detecting a style of textual documents. However, only a few of them dealt with web documents. In this paper we suggest sets of features to detect styles of web documents. Web documents are different from textual documents in that Dey contain URL and HTML tags within the pages. We introduce the features specific to web documents, which are extracted from URL and HTML tags. Experimental results enable us to evaluate their characteristics and performances.

• Journal of the Korean Society for information Management
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• v.36 no.2
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• pp.57-77
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• 2019

Random Forest (RF), a representative ensemble technique, was applied to automatic classification of journal articles in the field of library and information science. Especially, I performed various experiments on the main factors such as tree number, feature selection, and learning set size in terms of classification performance that automatically assigns class labels to domestic journals. Through this, I explored ways to optimize the performance of random forests (RF) for imbalanced datasets in real environments. Consequently, for the automatic classification of domestic journal articles, Random Forest (RF) can be expected to have the best classification performance when using tree number interval 100~1000(C), small feature set (10%) based on chi-square statistic (CHI), and most learning sets (9-10 years).

• Annual Conference on Human and Language Technology
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• 1998.10c
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• pp.91-96
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• 1998

본 논문에서는 주제범주 체계를 이용한 웹 검색이 가지는 장점을 이용 할 수 있도록 인터넷 웹 페이지들을 주제범주 체계에 따라 자동으로 분류하는 모델을 제시한다. 특히 웹 페이지 작성자들의 의도를 범주화에 반영할 수 있는 방법으로 HTML 태그를 이용한다. 즉 웹 페이지의 표현에 있어서 벡터 스페이스 모델에서의 색인어 빈도 가중치에 태그 가중치를 추가 하여 보다 좋은 성능을 얻도록 하였다. 그리고 주제범주를 표현하는데 사용되는 자질의 선정에는 기대상호정보, 상호정보 척도를, 문서간 유사도 비교에는 최근린법을 사용하였다. 전북대에서 정보탐정용으로 분류한 웹 페이지를 대상으로 실험하였으며, 기본 모델 대비 약 7%의 정확도 향상을 얻을 수 있었다.

• Journal of Korean Library and Information Science Society
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• v.42 no.3
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• pp.149-164
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• 2011

This study is to represent the research trends of the resource organization in Korea through the analysis of 607 papers related with resource organization in 4,015 papers on journals of 6 library & information science societies from 1970 through 2010. The results of this study are as follows. The average yearly papers in the case of academic journals published 14.8 pieces. The year published the largest number of papers: 2005 and 2009. The order of the number of papers by the domain of resource organization: cataloging, classification, indexing and abstracting, metadata, subject analysis. And the research on basic principle or theory in the resource organization showed insufficient. The research on KDC classification, cataloging rule, metadata element has are usually presented for improvements. But most of the research is not empirical analysis or objective assessment but subjective judgments of the researchers.

• Korean Journal of Cognitive Science
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• v.26 no.3
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• pp.263-278
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• 2015

Online Q&A for the National Institute of the Korean Language provides expert's answers for questions about the Korean language, in which many similar questions are repeatedly posted like other Q&A boards. So, if a system automatically finds questions that are similar to a user's question, it can immediately provide users with recommendable answers to their question and prevent experts from wasting time to answer to similar questions repeatedly. In this paper, we set 5 classes of questions based on its topic which are frequently asked, and propose to classify questions to those classes. Our system searches similar questions by combining topic similarity, vector similarity and sequence similarity. Experiment shows that our method improves search correctness with topic classification. In experiment, Mean Reciprocal Rank(MRR) of our system is 0.756, and precision for the first result is 68.31% and precision for top five results is 87.32%.