• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.81-86
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• 2016

Term weighting is a popular technique that effectively weighs the term features to improve accuracy in document classification. While several successful term weighting algorithms have been suggested, none of them appears to perform well consistently across different data domains. In this paper we propose several reasonable methods to combine different term weight vectors to yield a robust document classifier that performs consistently well on diverse datasets. Specifically we suggest two approaches: i) learning a single weight vector that lies in a convex hull of the base vectors while minimizing the class prediction loss, and ii) a mini-max classifier that aims for robustness of the individual weight vectors by minimizing the loss of the worst-performing strategy among the base vectors. We provide efficient solution methods for these optimization problems. The effectiveness and robustness of the proposed approaches are demonstrated on several benchmark document datasets, significantly outperforming the existing term weighting methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.9
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• pp.2155-2160
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• 2014

In this paper, we proposes a new document summarization method using the extracted semantic feature which the semantic feature is extracted by distributed parallel processing based Hadoop. The proposed method can well represent the inherent structure of documents using the semantic feature by the non-negative matrix factorization (NMF). In addition, it can summarize the big data document using Hadoop. The experimental results demonstrate that the proposed method can summarize the big data document which a single computer can not summarize those.

• The KIPS Transactions:PartB
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• v.8B no.6
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• pp.657-668
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• 2001

It is difficult that web documents are classified with exact user intention because existing document classification systems are based on word frequency number using single keyword. To improve this defect, first, we use keyword, a query, domain knowledge. Like explanation based learning, first, query is analyzed with knowledge based information and then structured user intention information is extracted. We use this intention tree in the course of existing word frequency number based document classification as user information and constraints. Thus, we can classify web documents with more exact user intention. In classifying document, structured user intention information is helpful to keep more documents and information which can be lost in the system using single keyword information. Our hybrid approach integrating user intention information with existing statistics and probability method is more efficient to decide direction and range of document category than existing word frequency approach.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.347-358
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• 2003

VSField-associated term is a single or compound word whose terms occur in any document, and which makes it possible to recognize a field of text by using common knowledge of human. For example, human recognizes the field of document such as or , a field name of text, when she encounters a word 'Pitcher' or 'election', respectively We Proposes an efficient construction method of field-associated terms (FTs) for specializing field to decide a field of text. We could fix document classification scheme from well-classified document database or corpus. Considering focus field we discuss levels and stability ranks of field-associated terms. To construct a balanced FT collection, we construct a single FTs. From the collections we could automatically construct FT's levels, and stability ranks. We propose a new extraction algorithms of FT's for document classification by using FT's concentration rate, its occurrence frequencies.

• Journal of KIISE
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• v.44 no.2
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• pp.179-185
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• 2017

Document summarization aims to generate a summary that is consistent and contains the highly related sentences in a document. In this study, we implemented for document summarization that extracts highly related sentences from a whole document by considering both similarities and entailment relations between sentences. Accordingly, we proposed a new algorithm, TextRank-NLI, which combines a Recurrent Neural Network based Natural Language Inference model and a Graph-based ranking algorithm used in single document extraction-based summarization task. In order to evaluate the performance of the new algorithm, we conducted experiments using the same datasets as used in TextRank algorithm. The results indicated that TextRank-NLI showed 2.3% improvement in performance, as compared to TextRank.

• Journal of the Korean Society for information Management
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• v.40 no.1
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• pp.1-21
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• 2023

This study specifically reviewed the performance of the ranking schemes as an efficient feature selection method for text classification. Until now, feature ranking schemes are mostly based on document frequency, and relatively few cases have used the term frequency. Therefore, the performance of single ranking metrics using term frequency and document frequency individually was examined as a feature selection method for text classification, and then the performance of combination ranking schemes using both was reviewed. Specifically, a classification experiment was conducted in an environment using two data sets (Reuters-21578, 20NG) and five classifiers (SVM, NB, ROC, TRA, RNN), and to secure the reliability of the results, 5-Fold cross-validation and t-test were applied. As a result, as a single ranking scheme, the document frequency-based single ranking metric (chi) showed good performance overall. In addition, it was found that there was no significant difference between the highest-performance single ranking and the combination ranking schemes. Therefore, in an environment where sufficient learning documents can be secured in text classification, it is more efficient to use a single ranking metric (chi) based on document frequency as a feature selection method.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.35-46
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• 2023

In this study, we propose a Dual Approach methodology to enhance the accuracy of document classifiers by utilizing both contextual and keyword information. Firstly, contextual information is extracted using Google's BERT, a pre-trained language model known for its outstanding performance in various natural language understanding tasks. Specifically, we employ KoBERT, a pre-trained model on the Korean corpus, to extract contextual information in the form of the CLS token. Secondly, keyword information is generated for each document by encoding the set of keywords into a single vector using an Autoencoder. We applied the proposed approach to 40,130 documents related to healthcare and medicine from the National R&D Projects database of the National Science and Technology Information Service (NTIS). The experimental results demonstrate that the proposed methodology outperforms existing methods that rely solely on document or word information in terms of accuracy for document classification.

• Journal of Internet Computing and Services
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• v.7 no.6
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• pp.123-130
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• 2006

The transaction was happened within a single business entity or a single marketplace at the stage of e-business. It becomes to grow to complex form. Expecially, the need for business collaboration between business entities or marketplaces has being on the rise as the core topic. The format translation between documents is very important factor according to various the exchanged document formats. In this paper, we define ebXML as the basic format of exchanged document according to object-oriented business transaction. Also we design the multi-format translation system to support the translation of various document formats. The proposed system in this paper, is designed with model-driven method and it is possible to construct with various structure as for system environment. The proposed translation system is designed to use the proposed system as adding the corresponding parsing module even though any format of document. Also, we increase the reusability of data as using the common data set. In this paper, we prove the superiority of the proposed system to compare the performance with the legacy system for various format translation.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 1997.11a
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• pp.354-367
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• 1997

Chaum's blind signature scheme is the typical withdrawal procedure of electronic cash. In blind signature scheme, a signer sign a document while he never knows the content of the signed document. Yung and Jakobsson presented new signature scheme with which the content of document can be unblinded if unusual activity is detected. This signature is referred to as the magic ink signature. In this paper, we analyze the magic ink signature, and present two new magic ink signature schemes using KCDSA and Schnorr signature algorithm. We propose two types of the efficient electonic cash system using these magic ink signature schemes. One is the electronic cash system with a single server magic init signature scheme, and the other is the electronic cash system with a distributed magic ink signature scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.5
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• pp.730-738
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• 1993

This paper proposes a dynamic formatting method for processing large amounts of document in a device independent manner. And it is very useful for cross-referencing among pages in a single document and for presenting multiple pages simultaneously. The method can be applied usefully to hypertext's application such as establishing a link and a cross-reference among pages in a multiple document. We implemented an electronic publishing system of WYSIWYG type using X window system and Motif graphical user interface.