• Journal of Information Management
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• v.33 no.2
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• pp.19-32
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• 2002

With the growth of the digital library and the use of Internet, the amount of online text information has increased rapidly. The need for efficient data management and retrieval techniques has also become greater. An automatic text categorization system assigns text documents to predefined categories. The system allows to reduce the manual labor for text categorization. In order to classify text documents, the good features from the documents should be selected and the documents are indexed with the features. In this paper, each steps of text categorization and several techniques used in each step are introduced.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.345-359
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• 2017

Optical Character Recognition (OCR) that has been a main research topic of computer vision and artificial intelligence now extend its applications to detection of text area from video or image contents taken by camera devices and retrieval of text information from the area. This paper aims to implement a binarization algorithm that removes user intervention and provides robust performance to outdoor lights by using TopHat algorithm and channel transformation technique. In this study, we particularly concentrate on text information of outdoor signboards and validate our proposed technique using those data.

• The Journal of Society for e-Business Studies
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• v.14 no.4
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• pp.59-73
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• 2009

Keyword extraction is an important and essential technique for text mining applications such as information retrieval, text categorization, summarization and topic detection. A set of keywords extracted from a large-scale electronic document data are used for significant features for text mining algorithms and they contribute to improve the performance of document browsing, topic detection, and automated text classification. This paper presents a keyword extraction technique that can be used to detect topics for each news domain from a large document collection of internet news portal sites. Basically, we have used six variants of traditional TF-IDF weighting model. On top of the TF-IDF model, we propose a word filtering technique called 'cross-domain comparison filtering'. To prove effectiveness of our method, we have analyzed usefulness of keywords extracted from Korean news articles and have presented changes of the keywords over time of each news domain.

• Korean Journal of Cognitive Science
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• v.4 no.1
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• pp.213-242
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• 1993

During the past decades,several automatic indexing systems have been developed such as single term indexing.phrase indexing and thesaurus basedidndexing systems.Among these systems,single term indexing has been known as superior to others despte its simpicity of extracting meaningful terms.On the other hand,thesaurus based one has been conceived as producing low retrival rate ,mainly because thesauri do not usually have enough index terms.so that much of text data fail to be indexed if they do not match with any of index terms in thesauri.This paper develops a thesaurus based indexing system THINS that yields higher retrieval rate than other systems.by doing syntactic analysis of text data and matching them with index terms in thesauri partially.First,the system analyzes the input text syntactically by using the machine translation suystem MATES/EK and extracts noun phrases.After deleting stop words from noun phrases and stemming the remaining ones.it tries to index these with similar index terms in the thesaurus as much as possible. We conduct an experiment with CACM data set that measures the retrieval effectiveness with CACM data set that measures the retrieval effectuvenss of THINS with single term based one under HYKIS-a thesaurus based information retrieval system.It turns out that THINS yields about 10 percent higher precision than single term based one.while shows 8to9 percent lower recall.This retrieval rate shows that THINS improves much better than privious ones that only yields 25 or 30 percent lower precision than single term based one.We also argue that the relatively lower recall is cause by that CRCS-the thesaurus included in CACM datea set is very incomplete one,having only more than one thousand terms,thus THINS is expected to produce much higher rate if it is associated with currently available large thesaurus.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.23-33
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• 2014

The development of the information retrieval field is evolving to the research field searching accurately for the information from thing finding rapidly a large amount of information. Personalization and the semantic web technology is a key technology. The automatic indexing technology about the web document and throughput go beyond the research stage and show up as the practical service. However, there is a lack of research on the document information retrieval field about the attached document type of except the web document. In this paper, we illustrate about the method in which it analyzed the text content of the unstructured documents prepared in the text, word, hwp form and it how to construction OWL ontology. To build TBox of the document ontology and the resources which can be obtained from the document is selected, and we implement with the system in order to utilize as the instant of the constructed document ontology. It is effectually usable in the information retrieval and document management system using the semantic technology of the correspondence document as the ontology automatic construction of this kind of the unstructured documents.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.159-165
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• 2019

Based on deep learning technology, speech recognition method has began to be applied to commercial products, but it is still difficult to be used in the area of VR contents, since there is no easy and efficient way to process the recognized text after the speech recognition module. In this paper, we propose a Korean Language Command System, which can efficiently recognize and respond to Korean speech commands. The system consists of two components. One is a morphological analyzer to analyze sentence morphemes and the other is a retrieval based model which is usually used to develop a chatbot system. Experimental results shows that the proposed system requires only 16% commands to achieve the same level of performance when compared with the conventional string comparison method. Furthermore, when working with Google Cloud Speech module, it revealed 60.1% of success rate. Experimental results show that the proposed system is more efficient than the conventional string comparison method.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.6
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• pp.614-618
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• 2008

As the multimedia data increase a lot with the rapid development of the Internet, an efficient retrieval technique focusing on individual users is required based on the analyses of such data. However, user modeling services provided by recent web sites have the limitation of text-based page configurations and recommendation retrieval. In this paper, we construct the user preference model with a Bayesian network to apply the user modeling to video retrieval, and suggest a method which utilizes probability reasoning. To do this, context information is defined in a real office environment and the video scripts acquired from established cameras and annotated the context information manually are used. Personal information of the user, obtained from user input, is adopted for the evidence value of the constructed Bayesian Network, and user preference is inferred. The probability value, which is produced from the result of Bayesian Network reasoning, is used for retrieval, making the system return the retrieval result suitable for each user's preference. The usability test indicates that the satisfaction level of the selected results based on the proposed model is higher than general retrieval method.

• Journal of Korean Library and Information Science Society
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• v.32 no.1
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• pp.249-260
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• 2001

This paper presents the application of best match search algorithm in the client/server system for natural language access to Web-based database. For this purpose, the procedures to process Korean word variants as well as to execute probabilistic weighting scheme have been implemented in the client/server system. The experimental runs have been done using a Korean test set which included documents, queries and relevance judgements. The experimental results demonstrate that best match retrieval with relevance information is better than the retrieval without it.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1553-1567
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• 2021

Steganography is a current hot research topic in the area of information security and privacy protection. However, most previous steganography methods are not effective against steganalysis and attacks because they are usually carried out by modifying covers. In this paper, we propose an improved coverless text steganography algorithm based on pretreatment and Part of Speech (POS), in which, Chinese character components are used as the locating marks, then the POS is used to hide the number of keywords, the retrieval of stego-texts is optimized by pretreatment finally. The experiment is verified that our algorithm performs well in terms of embedding capacity, the embedding success rate, and extracting accuracy, with appropriate lengths of locating marks and the large scale of the text database.

• International Journal of Contents
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• v.13 no.4
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• pp.70-79
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• 2017

Images are an important element in patents and many experts use images to analyze a patent or to check differences between patents. However, there is little research on image analysis for patents partly because image processing is an advanced technology and typically patent images consist of visual parts as well as of text and numbers. This study suggests two methods for using image processing; the Scale Invariant Feature Transform(SIFT) algorithm and Optical Character Recognition(OCR). The first method which works with SIFT uses image feature points. Through feature matching, it can be applied to calculate the similarity between documents containing these images. And in the second method, OCR is used to extract text from the images. By using numbers which are extracted from an image, it is possible to extract the corresponding related text within the text passages. Subsequently, document similarity can be calculated based on the extracted text. Through comparing the suggested methods and an existing method based only on text for calculating the similarity, the feasibility is achieved. Additionally, the correlation between both the similarity measures is low which shows that they capture different aspects of the patent content.