• Journal of KIISE:Software and Applications
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• v.33 no.2
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• pp.243-251
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• 2006

In multi-document summarization by means of salient sentence extraction, it is important to remove redundant information. In the removal process, the similarities and differences of sentences are considered. In this paper, we propose a method for multi-document summarization which extracts salient sentences without having redundant sentences by way of cohesive term clustering method that utilizes co-occurrence Information. In the cohesive term clustering method, we assume that each term does not exist independently, but rather it is related to each other in meanings. To find the relations between terms, we cluster sentences according to topics and use the co-occurrence information oi terms in the same topic. We conduct experimental tests with the DUC(Document Understanding Conferences) data. In the tests, our method shows better performance of summarization than other summarization methods which use term co-occurrence information based on term cohesion of document or sentence unit, and simple statistical information.

• Journal of the Korean Society for information Management
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• v.24 no.1 s.63
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• pp.251-271
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• 2007

This paper studies the problem of classifying documents with labeled and unlabeled learning data, especially with regards to using document similarity features. The problem of using unlabeled data is practically important because in many information systems obtaining training labels is expensive, while large quantities of unlabeled documents are readily available. There are two steps In general semi-supervised learning algorithm. First, it trains a classifier using the available labeled documents, and classifies the unlabeled documents. Then, it trains a new classifier using all the training documents which were labeled either manually or automatically. We suggested two types of semi-supervised learning algorithm with regards to using document similarity features. The one is one step semi-supervised learning which is using unlabeled documents only to generate document similarity features. And the other is two step semi-supervised learning which is using unlabeled documents as learning examples as well as similarity features. Experimental results, obtained using support vector machines and naive Bayes classifier, show that we can get improved performance with small labeled and large unlabeled documents then the performance of supervised learning which uses labeled-only data. When considering the efficiency of a classifier system, the one step semi-supervised learning algorithm which is suggested in this study could be a good solution for improving classification performance with unlabeled documents.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.743-750
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• 2003

In this paper, we propose a query expansion and weighting method for document filtering to increase precision of the result of Web search engines. Query expansion for document filtering uses ConceptNet, encyclopedia and documents of 10% high similarity. Term weighting method is used for calculation of query-documents similarity. In the first step, we expand an initial query into the first expanded query using ConceptNet and encyclopedia. And then we weight the first expanded query and calculate the first expanded query-documents similarity. Next, we create the second expanded query using documents of top 10% high similarity and calculate the second expanded query- documents similarity. We combine two similarities from the first and the second step. And then we re-rank the documents according to the combined similarities and filter off non-relevant documents with the lower similarity than the threshold. Our experiments showed that our document filtering method results in a notable improvement in the retrieval effectiveness when measured using both precision-recall and F-Measure.

• Iberoamérica
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• v.23 no.1
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• pp.79-111
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• 2021

The purpose of this article is to present the potential of balangandãs as a documentary source for intersectional studies of gender and slavery, from the analysis of formal and symbolic aspects of the museum objects in the Museu Nacional Nacional (MHN) and Museu Carlos Costa Pinto (MCCP). Balangandãs are a type of creole jewelry, made in gold or silver and used in Brazil since the 18th century by black women - women who worked, especially in the commercialization of foodstuffs in large urban centers. They are described in printed sources and engravings, and preserved in some museum institutions. In the study, the meanings attributed to the object over the centuries are observed: jewelry, amulet, peculium, document, travel memory and heritage. As a result, the article seeks to highlight the objects as a historical and documentary source, verifying similarities between the pieces musealized at the MHN and at the MCCP, also emphasizing the documental power of the pieces produced today.

• Journal of the Korean Society for information Management
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• v.12 no.1
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• pp.87-97
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• 1995

Boolean retrieval systems have been most widely used in the area of information retrieval due to easy implementation and efficient retrieval. Conventional Boolean retrieval systems. however, cannot rank retrieved documents in decreasing order of query-document similarities because they cannot compute similarity coefficients between queries and documents. Extended Boolean models such as fuzzy set. Waller-Kraft, Paice, P-Norm and Infinite-One have been developed to provide the document ranking facility. In extended Boolean models, the formulas evaluating Boolean operators AND and OR are an important component to affect the quality of document ranking. In this paper we present mathematical properties of the formulas, and analyse their effect on retrieval effectiveness. Our analyses show that P-Norm is the most suitable for achieving high retrieval effectiveness.

• Journal of Korea Multimedia Society
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• v.22 no.11
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• pp.1313-1323
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• 2019

With the development of the Internet and the increase of smart phones, various services considering user convenience are increasing, so that users can check news in real time anytime and anywhere. However, online news is categorized by media and category, and it provides only a few related search terms, making it difficult to find related news related to keywords. In order to solve this problem, we propose a method to recommend related documents more accurately by applying Doc2Vec similarity to the specific keywords of news articles and weighting the title and contents of news articles. We collect news articles from Naver politics category by web crawling in Java environment, preprocess them, extract topics using LDA modeling, and find similarities using Doc2Vec. To supplement Doc2Vec, we apply TF-IDF to obtain TC(Title Contents) weights for the title and contents of news articles. Then we combine Doc2Vec similarity and TC weight to generate TC weight-similarity and evaluate the similarity between words using PMI technique to confirm the keyword association.

• The Journal of the Korea Contents Association
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• v.7 no.5
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• pp.16-26
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• 2007

Because it is difficult on existing document retrieval systems to visualize the search result, search results show document titles and short summaries of the parts that include the search keywords. If the result list is long, it is difficult to examine all the documents at once and to find a relation among them. This study uses clustering to classify similar documents into groups to make it easy to grasp the relations among the searched documents. Also, this study proposes a two-level visualization algorithm such that, first, the center of clusters is projected to low-dimensional space by using multi-dimensional scaling to help searchers grasp the relation among clusters at a glance, and second, individual documents are drawn in low-dimensional space based on the center of clusters using the orbital model as a basis to easily confirm similarities among individual documents. This study is tested on the benchmark data and the real data, and it shows that it is possible to visualize search results in real time.

• Journal of the Korean Society for Library and Information Science
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• v.44 no.3
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• pp.155-173
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• 2010

In this study, a new keyword extraction algorithm is applied to a single document with term clustering. A single document is divided by multiple passages, and two ways of calculating similarities between two terms are investigated; the first-order similarity and the second-order distributional similarity. In this experiment, the best cluster performance is achieved with a 50-term passage from the second-order distributional similarity. From the results of first experiment, the second-order distribution similarity was also applied to various keyword extraction methods using statistic information of terms. In the second experiment, pf(paragraph frequency) and $tf{\times}ipf$(term frequency by inverse paragraph frequency) were found to improve the overall performance of keyword extraction. Therefore, it showed that the algorithm fulfills the necessary conditions which good keywords should have.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.4
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• pp.25-31
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• 2007

An automatic term-network construction system is proposed in this paper. This system uses the statistical values of the terms appeared in a document corpus. The 186 oral history documents collected from the Saemangeum area of Chollapuk-do, Korea, are used for the research. The term relationships presented in the term-network are decided by the cosine similarities of the term vectors. The number of the terms extracted from the documents is about 1700. The system is able to show the term relationships from the term-network as quickly as like a real-time system. The way of this term-network construction is expected as one of the methods to construct the ontology system and to support the semantic retrieval system in the near future.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.6
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• pp.538-549
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• 2005

This paper proposes a method of generating XSLT scripts, which support the fast transformation of XML documents, given one-to-one matching relationships between leaf nodes of XML schemas. The proposed method consists of two steps: computing matchings between cardinality nodes and generating XSLT scripts. Matching relationships between cardinality nodes are computed by using Proposed lexical and structural similarities. Based on the cardinality node matching relationships, an XSLT script is generated. Experimental results show that the proposed method has generated XSLT scripts that support the faster transformation of XML documents, compared with previous works.