• The KIPS Transactions:PartB
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• v.10B no.2
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• pp.229-236
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• 2003

Named entity recognition is the process indispensable to question answering and information extraction systems. This paper presents an HMM based named entity (m) recognition method using the construction principles of compound words. In Korean, many named entities can be decomposed into more than one word. Moreover, there are contextual relationships among nouns in an NE, and among an NE and its surrounding words. In this paper, we classify words into a word as an NE in itself, a word in an NE, and/or a word adjacent to an n, and train an HMM based on NE-related word types and parts of speech. Proposed named entity recognition (NER) system uses trigram model of HMM for considering variable length of NEs. However, the trigram model of HMM has a serious data sparseness problem. In order to solve the problem, we use multi-level back-offs. Experimental results show that our NER system can achieve an F-measure of 87.6% in the economic articles.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.10C
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• pp.1000-1010
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• 2006

This paper proposes a new architecture for DCT-based motion estimation and compensation. Previous methods do riot take sufficient advantage of the sparseness of 2-D DCT coefficients to reduce execution time. We first derive a recursion equation to perform DCT domain motion estimation more efficiently; we then use it to develop a wavefront array processor (WAP) consisting of processing elements. In addition, we show that the recursion equation enables motion predicted images with different frequency bands, for example, from the images with low frequency components to the images with low and high frequency components. The wavefront way Processor can reconfigure to different motion estimation algorithms, such as logarithmic search and three step search, without architectural modifications. These properties can be effectively used to reduce the energy required for video encoding and decoding. The proposed WAP architecture achieves a significant reduction in computational complexity and processing time. It is also shown that the motion estimation algorithm in the transform domain using SAD (Sum of Absolute Differences) matching criterion maximizes PSNR and the compression ratio for the practical video coding applications when compared to tile motion estimation algorithm in the spatial domain using either SAD or SSD.

• Journal of KIISE
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• v.43 no.8
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• pp.927-932
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• 2016

With the explosive increase in the number of online medical documents, the demand for question-answering systems is increasing. Recently, question-answering models based on machine learning have shown high performances in various domains. However, many question-answering models within the medical domain are still based on information retrieval techniques because of sparseness of training data. Based on various information retrieval techniques, we propose an answer snippet retrieval model for question-answering systems of medical documents. The proposed model first searches candidate answer sentences from medical documents using a cluster-based retrieval technique. Then, it generates reliable answer snippets using a re-ranking model of the candidate answer sentences based on various sentence retrieval techniques. In the experiments with BioASQ 4b, the proposed model showed better performances (MAP of 0.0604) than the previous models.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.75-86
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• 2006

Target word selection is one of the most important and difficult tasks in English-Korean Machine Translation. It effects on the translation accuracy of machine translation systems. In this paper, we present a new approach to select Korean target word for an English noun with translation ambiguities using multiple knowledge such as verb frame patterns, sense vectors based on collocations, statistical Korean local context information and co-occurring POS information. Verb frame patterns constructed with dictionary and corpus play an important role in resolving the sparseness problem of collocation data. Sense vectors are a set of collocation data when an English word having target selection ambiguities is to be translated to specific Korean target word. Statistical Korean local context Information is an N-gram information generated using Korean corpus. The co-occurring POS information is a statistically significant POS clue which appears with ambiguous word. The experiment showed promising results for diverse sentences from web documents.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.205-211
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• 2012

The retrieved documents have to be transformed into proper data structure for the clustering algorithms of statistics and machine learning. A popular data structure for document clustering is document-term matrix. This matrix has the occurred frequency value of a term in each document. There is a sparsity problem in this matrix because most frequencies of the matrix are 0 values. This problem affects the clustering performance. The sparseness of document-term matrix decreases the performance of clustering result. So, this research uses the factor score by factor analysis to solve the sparsity problem in document clustering. The document-term matrix is transformed to document-factor score matrix using factor scores in this paper. Also, the document-factor score matrix is used as input data for document clustering. To compare the clustering performances between document-term matrix and document-factor score matrix, this research applies two typed matrices to self organizing map (SOM) clustering.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.965-978
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• 2006

This paper implements an automatic Korean word-spacing system based on word-recognition using morpheme unigrams and the pattern that the categories of those morpheme unigrams share within a candidate word. Although previous work on Korean word-spacing models has produced the advantages of easy construction and time efficiency, there still remain problems, such as data sparseness and critical memory size, which arise from the morpho-typological characteristics of Korean. In order to cope with both problems, our implementation uses the stochastic information of morpheme unigrams, and their category patterns, instead of word unigrams. A word's probability in a sentence is obtained based on morpheme probability and the weight for the morpheme's category within the category pattern of the candidate word. The category weights are trained so as to minimize the error means between the observed probabilities of words and those estimated by words' individual-morphemes' probabilities weighted according to their categories' powers in a given word's category pattern.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.2
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• pp.113-120
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• 2024

The population sparseness index(PSI) is being utilized to describe the functioning of internal layers in artificial neural networks from the perspective of neurons, shedding light on the black-box nature of the network's internal operations. There is research indicating a positive correlation between the PSI and performance in each layer of convolutional neural network models for image classification. In this study, we observed the internal operations of a convolutional neural network when adversarial examples were applied. The results of the experiments revealed a similar pattern of positive correlation for adversarial examples, which were modified to maintain 5% accuracy compared to applying benign data. Thus, while there may be differences in each adversarial attack, the observed PSI for adversarial examples demonstrated consistent positive correlations with benign data across layers.

• Journal of KIISE:Software and Applications
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• v.33 no.12
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• pp.1073-1089
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• 2006

The goal of Natural Language Processing(NLP) is to make a computer understand a natural language and to deliver the meanings of natural language to humans. Word sense Disambiguation(WSD is a very important technology to achieve the goal of NLP. In this paper, we describe a technology for automatic homonyms disambiguation using both Mutual Information(MI) and a Sense-Tagged Compound Noun Dictionary. Previous research work using word definitions in dictionary suffered from the problem of data sparseness because of the use of exact word matching. Our work overcomes this problem by using MI which is an association measure between words. To reflect language features, the rate of word-pairs with MI values, sense frequency and site of word definitions are used as weights in our system. We constructed a Sense-Tagged Compound Noun Dictionary for high frequency compound nouns and used it to resolve homonym sense disambiguation. Experimental data for testing and evaluating our system is constructed from QA(Question Answering) test data which consisted of about 200 query sentences and answer paragraphs. We performed 4 types of experiments. In case of being used only MI, the result of experiment showed a precision of 65.06%. When we used the weighted values, we achieved a precision of 85.35% and when we used the Sense-Tagged Compound Noun Dictionary, we achieved a precision of 88.82%, respectively.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2819-2830
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• 2009

Load bearing structural members in a wide variety of applications accumulate damage over their service life. During experiment much effort and cost is needed for measuring structural safety assessment. The sparseness and errors of measured data have to be considered during the safety estimation of structures. This paper introduces parameter estimation and damage identification algorithm by a system identification using static and dynamic response. The equation error estimator and response error widely used in system identification are based on the minimization of least squared error between measured and calculated responses by a mathematical model of a structure. Since each estimator has a specific form of application in noisy environment and proposes different definitions for these forms. To study the behaviour of the estimators in noisy environment Using Monte Carlo simulation, and a data measured pertubation scheme is adopted to investigate the influence of measurement errors on identification results. The assessment result by static and dynamic response were compared, and the efficiency and applicabilities of the proposed algorithm are demonstrated through simulated static and dynamic responses of a dimensional truss type structures.

• Journal of KIISE:Software and Applications
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• v.27 no.7
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• pp.769-783
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• 2000

In this paper, in order to disambiguate Korean noun word sense, we define a local context and explain how to extract it from a raw corpus. Following the intuition that two different nouns are likely to have similar meanings if they occur in the same local context, we use, as a clue, the word that occurs in the same local context where the target noun occurs. This method increases the usability of extracted knowledge and makes it possible to disambiguate the sense of infrequent words. And we can overcome the data sparseness problem by extending the verbs in a local context. The sense of a target noun is decided by the maximum similarity to the clues learned previously. The similarity between two words is computed by their concept distance in the sense hierarchy borrowed from WordNet. By reducing the multiplicity of clues gradually in the process of computing maximum similarity, we can speed up for next time calculation. When a target noun has more than two local contexts, we assign a weight according to the type of each local context to implement the differences according to the strength of semantic restriction of local contexts. As another knowledge source, we get a co-occurrence information from dictionary definitions and example sentences about the target noun. This is used to support local contexts and helps to select the most appropriate sense of the target noun. Through experiments using the proposed method, we discovered that the applicability of local contexts is very high and the co-occurrence information can supplement the local context for the precision. In spite of the high multiplicity of the target nouns used in our experiments, we can achieve higher performance (89.8%) than the supervised methods which use a sense-tagged corpus.