• Journal of Distribution Science
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• v.11 no.11
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• pp.57-61
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• 2013

Purpose - This study aimed to understand the influence of visible factors directly shown to and experienced by consumers such as physical evidence, and invisible factors obtained through acquaintances or other experienced consumers such as word-of-mouth, in the discount stores' marketing communication on impulse buying intention and store loyalty. Research Design, Data, and Methodology - This study examined the effect of factors in discount stores' marketing communication, for instance, physical evidence, word-of-mouth, and buying impulse intention. The questionnaire survey resulted in 68 completed questionnaires. Results - Physical evidence and word-of-mouth have a statistically significant positive effect on store loyalty. The results of regression analysis regarding whether visible or invisible factors have more impact showed that word-of-mouth has a statistically significant positive effect on store loyalty. With regard to impulse buying intention, only word-of-mouth was statistically significant. Conclusion These results suggested that visible and invisible factors that appeal to customers are very important. In particular, the results suggested that stores should create invisible factors (e.g., positive word-of-mouth) for their customers.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.60-63
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• 2017

자연어처리에 딥 러닝을 적용하기 위해 사용되는 Word embedding은 단어를 벡터 공간상에 표현하는 것으로 차원축소 효과와 더불어 유사한 의미의 단어는 유사한 벡터 값을 갖는다는 장점이 있다. 이러한 word embedding은 대용량 코퍼스를 학습해야 좋은 성능을 얻을 수 있기 때문에 기존에 많이 사용되던 word2vec 모델은 대용량 코퍼스 학습을 위해 모델을 단순화 하여 주로 단어의 등장 비율에 중점적으로 맞추어 학습하게 되어 단어의 위치 정보를 이용하지 않는다는 단점이 있다. 본 논문에서는 기존의 word embedding 학습 모델을 단어의 위치정보를 이용하여 학습 할 수 있도록 수정하였다. 실험 결과 단어의 위치정보를 이용하여 word embedding을 학습 하였을 경우 word-analogy의 syntactic 성능이 크게 향상되며 어순이 바뀔 수 있는 한국어에서 특히 큰 효과를 보였다.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.60-63
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• 2017

자연어처리에 딥 러닝을 적용하기 위해 사용되는 Word embedding은 단어를 벡터 공간상에 표현하는 것으로 차원축소 효과와 더불어 유사한 의미의 단어는 유사한 벡터 값을 갖는다는 장점이 있다. 이러한 word embedding은 대용량 코퍼스를 학습해야 좋은 성능을 얻을 수 있기 때문에 기존에 많이 사용되던 word2vec 모델은 대용량 코퍼스 학습을 위해 모델을 단순화 하여 주로 단어의 등장 비율에 중점적으로 맞추어 학습하게 되어 단어의 위치 정보를 이용하지 않는다는 단점이 있다. 본 논문에서는 기존의 word embedding 학습 모델을 단어의 위치정보를 이용하여 학습 할 수 있도록 수정하였다. 실험 결과 단어의 위치정보를 이용하여 word embedding을 학습 하였을 경우 word-analogy의 syntactic 성능이 크게 향상되며 어순이 바뀔 수 있는 한국어에서 특히 큰 효과를 보였다.

• Journal of information and communication convergence engineering
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• v.17 no.3
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• pp.191-197
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• 2019

Morphological analysis is used for searching sentences and understanding context. As most morpheme analysis methods are based on predefined dictionaries, the problem of a target word not being registered in the given morpheme dictionary, the so-called unregistered word problem, can be a major cause of reduced performance. The current practical solution of such unregistered word problem is to add them by hand-write into the given dictionary. This method is a limitation that restricts the scalability and expandability of dictionaries. In order to overcome this limitation, we propose a novel method to automatically expand a dictionary by means of use-case analysis, which checks the validity of the unregistered word by exploring the use-cases through web crawling. The results show that the proposed method is a feasible one in terms of the accuracy of the validation process, the expandability of the dictionary and, after registration, the fast extraction time of morphemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.10
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• pp.1405-1411
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• 2018

This paper designs access control system for MS-word(Microsoft-word) document system. The system designed in this paper uses the document-related information by analyzing the MS-word document structure. It is designed to block access to users who can not access the modified information by partially modifying MS-word document information. This makes it impossible to read documents other than those who have access to the MS-word document. This allows you to control access to the MS-word document. A user with access to the MS-word document will be able to retrieve the modified information back to the original information so that the document can be read normally. In this paper, we design and implement experiments. In the experiment, we performed document access if MS-word document information was modified. Experimental results show that the MS-word access control system operates normally.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.373-386
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• 2014

A word cloud is a visualization of word frequency in a given text. The importance of each word is shown in font size or color. This plot is useful for quickly perceiving the most prominent words and for locating a word alphabetically to determine its relative prominence. With dynamic graphics, we can find the changing pattern of prominent words and their frequencies according to the changing selection of chapters in a given text. We can define the word frequency matrix. In this matrix, rows are chapters in text and columns are ranks corresponding to word frequency about the words in the text. We can draw the word frequency matrix plot with this matrix. Dynamic graphic can indicate the changing pattern of the word frequency matrix according to the changing selection of the range of ranks of words. We execute an English Bible text visualization using word clouds and dynamic graphics technology.

• MALSORI
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• no.59
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• pp.53-68
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• 2006

We report the evaluation results of the Korean speech recognition platform called ECHOS. The platform has an object-oriented and reusable architecture so that researchers can easily evaluate their own algorithms. The platform has all intrinsic modules to build a large vocabulary speech recognizer: Noise reduction, end-point detection, feature extraction, hidden Markov model (HMM)-based acoustic modeling, cross-word modeling, n-gram language modeling, n-best search, word graph generation, and Korean-specific language processing. The platform supports both lexical search trees and finite-state networks. It performs word-dependent n-best search with bigram in the forward search stage, and rescores the lattice with trigram in the backward stage. In an 8000-word continuous speech recognition task, the platform with a lexical tree increases 40% of word errors but decreases 50% of recognition time compared to the HTK platform with flat lexicon. ECHOS reduces 40% of recognition errors through incorporation of cross-word modeling. With the number of Gaussian mixtures increasing to 16, it yields word accuracy comparable to the previous lexical tree-based platform, Julius.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.771-788
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• 2019

Deep learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) show superior performance in text classification than traditional approaches such as Support Vector Machines (SVMs) and Naïve Bayesian approaches. When using CNNs for text classification tasks, word embedding or character embedding is a step to transform words or characters to fixed size vectors before feeding them into convolutional layers. In this paper, we propose a parallel word-level and character-level embedding approach in CNNs for text classification. The proposed approach can capture word-level and character-level patterns concurrently in CNNs. To show the usefulness of proposed approach, we perform experiments with two English and three Korean text datasets. The experimental results show that character-level embedding works better in Korean and word-level embedding performs well in English. Also the experimental results reveal that the proposed approach provides better performance than traditional CNNs with word-level embedding or character-level embedding in both Korean and English documents. From more detail investigation, we find that the proposed approach tends to perform better when there is relatively small amount of data comparing to the traditional embedding approaches.

• Koreanishche Zeitschrift fur Deutsche Sprachwissenschaft
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• v.6
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• pp.323-344
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• 2002

EuroNet ist eine multilinguale Datenbasis mit WordNets $f\"{u}r\;einige\;europ\"{a}ische$ Sprachen ($holl\"{a}ndisch$, italienisch, spanisch, deutsch, $franz\"{o}sisch$, tschechisch und estnisch). Die WordNets werden genauso wie das amerikanische WordNet $f\"{u}r$ Englisch (Princeton WordNet, Miller et al. 1990) in Synsets (Zusammensetzen der synonymen $W\"{o}rter$) mit grundlegenden lexikalisch-semantischen Relationen zwischen ihnen $ausgedr\"{u}ckt$ strukturiert. Jedes WordNet stellt also ein einzigartiges innersprachliches System $f\"{u}r$ die lexikalischen und konzeptuellen Relationen dar. $Zus\"{a}tzlich$ werden diese auf dem Princeton WordNet basierten WordNets (z.B. GermaNet) mit einem Inter-Linguale-Index (kurz, ILI) verbunden. $\"{U}ber$ diesem Index werden die Sprachen zusammengeschaltet, damit zu gehen ist $m\"{o}glich$, von den $W\"{o}rtern$ in einer Sprache zu den $\"{a}hnlichen\;W\"{o}rtern$ in jeder $m\"{o}glicher$ anderen Sprache. Der Index gibt auch Zugang zu einer geteilten Top-Ontologie von 63 semantischen Unterscheidungen. Diese Top-Ontologie stellt einen allgemeinen semantischen Rahmen $f\"{u}r$ aile Sprachen zur $Verf\"{u}gung,\;w\"{a}hrend$ sprachspezifische Eigenschaften in den einzelnen WordNets beibehalten werden. Die Datenbasis kann, unter anderen, $f\"{u}r$ einsprachige und multilinguale Informationsretrieval benutzt werden. In der vorliegenden Arbeit handelt sich also um eine methodologische Untersuchung der koreanisch-deutschen ILI-Verbindung zur Anwendung der auf dem EuroNet basierten lexikalischen, semantischen Datenbasis. Dabei werden einzelnen Lexeme in koreanischen, deutschen WordNets $zun\"{a}chst$ mit Hilfe der Sense-Analyse semantisch differenziert, und dann durch lexikalische und konzeptuelle Relationen(ILI) miteinander verbunden. Die Equivalezverbindungen dienen, sprachspezifische Konzepte zum ILI abzubilden. Sie werden von einem anderen Synset der moglichen Relationen aus der Euronet-Spezifikation genommen. Wenn es keinen ILI-Rekord gibt, der ein direktes Equivalenz zu einem gegebenen Konzept darstellt, kann das Konzept in der Frage $\"{u}ber$ EQ-Near-Synonymie, EQ-Hyperonymie oder EQ-Hyponymie Relationen verbunden werden.

• Journal of Korean Medical classics
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• v.29 no.3
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• pp.75-80
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• 2016

Objectives : The paper raises an objection to the word '涕' being used to refer to nasal discharge, and proposes a word for nasal discharge upon studying a set of medical books. Methods : The author finds and confirms the dictionary definition of '涕' and studies how they are used differently in medical books. Through this study, the author shows how the word '涕' is used incorrectly and makes deductions for its reason. The author takes a look at the old form of the word '涕', its etymological origin, takes a guess as to the real word that should have been used to refer to nasal discharge, and find examples of instances where this correct word for nasal discharge are more appropriate. Results & Conclusions : In medical books such as Huangdineijing Suwen, '涕' is used to mean nasal discharge, but the word's dictionary definition does not validate such usage. Yugunryeombu (劉君廉夫), in its commentary for Somun, used '?' and '鼻夷' for '涕', and '?' means nasal discharge and used as same as '涕' when its used to mean tear. This is a phenomenon that originated from '弟' and '夷' being used interchangeably which led to the incorrect usage of '?'. If someone were to refer to nasal discharge, he needs to use '?'. '鼻夷' is believed to be the same word as '弟鼻', which is the old form of '?', and it means both tear(pronounced 'Che') and nasal discharge(pronounced 'Je'). However, the pronunciation different between 'Che' and 'Je', and its definition as tear, is divided in later periods into '涕' following the shape of '弟'. Following the shape of '夷', the meaning of nasal discharge remains in '?' while retaining the pronunciation of 'yi'. Therefore, the word '涕' used to mean nasal discharge is an incorrect form of '?', and should all be re-written to '?'.