• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.563-568
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• 2016

The word sense disambiguation problem is to find the correct sense of an ambiguous word having multiple senses in a dictionary in a sentence. We regard this problem as a multi-class classification problem and classify the ambiguous word by using Support Vector Machines. Context words of the ambiguous word, which are extracted from Sejong sense tagged corpus, are represented to two kinds of vector space. One vector space is composed of context words vectors having binary weights. The other vector space has vectors where the context words are mapped by word embedding model. After experiments, we acquired accuracy of 87.0% with context word vectors and 86.0% with word embedding model.

• Journal of the Korean Society for information Management
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• v.24 no.1 s.63
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• pp.321-342
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• 2007

This study presents an effective word sense disambiguation model that does not require manual sense tagging Process by automatically tagging the right sense using a machine-readable and the collocation co-occurrence-based methods. The dictionary information-based method that applied multiple feature selection showed the tagging accuracy of 70.06%, and the collocation co-occurrence-based method 56.33%. The sense classifier using the dictionary information-based tagging method showed the classification accuracy of 68.11%, and that using the collocation co-occurrence-based tagging method 62.09% The combined 1a99ing method applying data fusion technique achieved a greater performance of 76.09% resulting in the classification accuracy of 76.16%.

• Culinary science and hospitality research
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• v.23 no.6
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• pp.143-152
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• 2017

This paper examined seemingly well-working compromises in identity construction, questioning whether the compromises could function only nominally in practice. The literature has paid attention to the conflicts which end up functionally sense-making, through either unilaterally enforced or mutually assimilated compromises. In contrast, this paper's analysis of Super Tuscan wines under the Italian government's quality regulation illustrated that the compromises between wineries and classification systems do not work well and make the classification systems meaningless in the end. This study thus argued that compromises in identity construction do not always result in functionally sense-making outcomes: they could be only nominal. This study suggested that idiosyncratic institutional contexts, such as weak organizational legacy, affect the results of identity construction in functional terms. At last, the theoretical and practical implications both in organization and management of this study were well discussed.

• Language and Information
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• v.9 no.2
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• pp.23-48
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• 2005

This study aims to discuss the word sense distinction of Korean middle verbs for restructuring KorLexVerb 1.0. Despite the duality of its meaning and syntactic structure, the word senses of middle verb are not clearly distinguished in current dictionaries. The underspecification causes very often mismatches that a same Korean word sense is used for two different English verb senses. A close examination on the syntactic and semantic properties of middle verb shows us that the word sense distinction and the reconstruction of hierarchical structure are indispensable. Finally, by doing this fine grained word sense distinction, we propose an alternative way of classification and description of the verb polysemy for KorLexVerb 1.0 as well as for dictionary-like language resources.

• Proceedings of the Korean Society for Language and Information Conference
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• 1998.02a
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• pp.236-247
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• 1998

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.100-105
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• 1992

Texture is one of the important characteristics that has been used to identify objects or regions of interest in an image. Tire tread patterns can be considered as a kind of texture, and these are classified with a texture analysis method. In this sense, this paper proposes a new algorithm for the classification of tire tread pattern. For the classification, cooccurrence matrix for the binary image is used. The performances are tested by experimentally 8 different tire tread pattern and the robustness is examined by including some kinds on noise.

• The Pure and Applied Mathematics
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• v.4 no.1
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• pp.7-18
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• 1997

Consider an $r\;\times\;c$ contingency table under the full multinomial model in which each classification is ordered. The problem is to test the null hypothesis of independence. A number of tests have been proposed for this problem. In this article we show that all of these tests can be improved on in some sense for most cases. In fact the preceding tests sometimes are inadmissible in a strict sense. Furthermore, we show by example that in some cases improved tests can yield substantially improved power functions.

• East Asian mathematical journal
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• v.37 no.4
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• pp.355-379
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• 2021

In this article, we investigated plane figures introduced in elementary school mathematics in the perspective of traditional classification, and also analyzed plane figures focused on the invariance of plane figures out of traditional classification. In the view of traditional classification, how to treat trapezoids was a key argument. In the current mathematics curriculum of the elementary school mathematics, the concept of sliding, flipping, and turning are introduced as part of development activities of spatial sense, but it is rare to apply them directly to figures. For example, how are squares and rectangles different in terms of symmetry? One of the main purposes of geometry learning is the classification of figures. Thus, the activity of classifying plane figures from a symmetrical point of view has sufficiently educational significance from Klein's point of view.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.198-204
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• 2021

Glioma is one of the common types of brain tumors starting in the brain's glial cell. These tumors are classified into low-grade or high-grade tumors. Physicians analyze the stages of brain tumors and suggest treatment to the patient. The status of the tumor has an importance in the treatment. Nowadays, computerized systems are used to analyze and classify brain tumors. The accurate grading of the tumor makes sense in the treatment of brain tumors. This paper aims to develop a classification of low-grade glioma and high-grade glioma using a deep learning algorithm. This system utilizes four transfer learning algorithms, i.e., AlexNet, GoogLeNet, ResNet18, and ResNet50, for classification purposes. Among these algorithms, ResNet18 shows the highest classification accuracy of 97.19%.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.7
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• pp.259-266
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• 2018

In this paper, we propose a method to classify a document using a Recurrent Neural Network by extracting features considering word sense and contexts. Word2vec method is adopted to include the order and meaning of the words expressing the word in the document as a vector. Doc2vec is applied for considering the context to extract the feature of the document. RNN classifier, which includes the output of the previous node as the input of the next node, is used as the document classification method. RNN classifier presents good performance for document classification because it is suitable for sequence data among neural network classifiers. We applied GRU (Gated Recurrent Unit) model which solves the vanishing gradient problem of RNN. It also reduces computation speed. We used one Hangul document set and two English document sets for the experiments and GRU based document classifier improves performance by about 3.5% compared to CNN based document classifier.